This research theme start its root from IROS 2005 Conference, where the overwhelming research work presented on SLAM  in addition to the upcoming industrial needs motivates rigorous work on this area. The first Master student worked on SLAM in the group was Ali Agha Mohammadi, who elaborates on different aspect of visual SLAM as well as implementation of Laser range finder based localization and mapping. Very soon other researchers explored a wide spectrum of research work on the consistency of EKF -based SLAM algorithms, as well as other state-of-the-art of techniques developed in this area such as FastSLAM. Some works are done on developing more suitable and faster optimization techniques being developed for iSAM algorithms.The research results was shortly used in different robotic platforms developed in the group. Among many works done in this area, one may mention the projects implemented on our Silver robot for exploration in an unknown indoor environment, further promoted for obstacle avoidance of static and dynamic objects. The implementation of SLAM algorithms in outdoor applications using stereo vision camera implemented on our other robotic platform Melon, was among the other challenges being fully worked out in the group. Soon we realized the importance and challenges existing in the 3D Mapping and localization, and a long term project was funded to develop a suitable 3D representation of the environment based on RGB-D sensory data. Using Kolmogorov complexity measures as well as Nurbs smoothing functions enables us to develop a very effective and computationally effective representation method for 3D visual data. Furthermore, trajectory planning and nonlinear control for navigation has been considered in the implementation of these techniques on autonomous ground robots as well as autonomous areal drones.

Research Topics

Mobile robot motion planning for search and exploration under uncertainty
Mobile robot motion planning for search and exploration under uncertainty

Many problems of the sequential decision making under uncertainty can be modeled as the Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) general frameworks. Motion planning under uncertainty is an instance of these problems. MDP and POMDP frameworks are computationally intractable and this problem restricts them to problems with small discrete state spaces and prevents using them in realistic applications. In this project, the motion planning is done for specific goals such as environment exploration, search and coverage. However, the presence of uncertainties makes them challenging tasks. In order to achieve a reliable plan and decision, these uncertainties should be considered in the robot’s planning and decision making. Therefore, the path planning for the exploration and search is modeled as an asymmetric Traveling Salesman Problem (aTSP) in the belief space in which the robot should search a series of goal points. Toward reducing the complexity of the aforementioned problem, the Feedback-based Information Roadmap (FIRM) is exploited. FIRM is a motion planning method for a robot operating under the motion and sensor uncertainty. FIRM provides a computationally tractable belief space planning and its capabilities make it suitable for real-time implementation and robust to the changing environment and large deviation. FIRM is proposed firstly by Dr. Ali Aghamohammadi as his Ph.D. thesis.

Using FIRM, the intractable traveling salesman optimization problem in the continuous belief space is changed to a simpler optimization problem on the TSP-FIRM graph. The optimal policy of the robot is obtained by finding the optimal path between each two goal points and solving the aTSP and then the policy is executed online. Also, Some algorithms are proposed to overcome the deviation from the path, kidnapping, finding new obstacles and becoming highly uncertain about the position which are possible situations in the online execution of the policy. Consequently, the robot should update its graph, map and policy online. The generic proposed algorithms are extended to the non-holonomic robots. In the online and offline phase, switching and LQG controllers as well as a Kalman filter for localization, are adopted. This algorithm can be implemented in practice and makes us one step closer to the solving Simultaneously Path planning, Localization and Mapping (SPLAM) problem.This algorithm is implemented in the Webots (video) and also on a real robot (Melon robot) (video). In both simulation and real implementation, we have used a vision-based localization based on the EKF.

Autonomous Flight and Obstacle Avoidance

Autonomous Flight and Obstacle Avoidance of a Quad rotor By Monocular SLAM

In this project, a monocular vision based autonomous flight and obstacle avoidance system for a commercial quad rotor is presented. The video stream of the front camera and the navigation data measured by the drone is sent to the ground station laptop via wireless connection. Received data processed by the vision based ORB-SLAM to compute the 3D position of the robot and the environment 3D sparse map in the form of point cloud. An algorithm is proposed for enrichment of the reconstructed map, and furthermore, a Kalman Filter is used for sensor fusion. The scaling factor of the monocular slam is calculated by the linear fitting. Moreover, a PID controller is designed for 3D position control. Finally, by means of the potential field method and Rapidly exploring Random Tree (RRT) path planning algorithm, a collision-free road map is generated. The proposed system enables the robot to flies autonomously in unknown environment and avoids colliding obstacles. The proposed algorithm generally consists of two parts. Firstly, we obtain the 3D position of robot. For this, the 3D position of robot is estimated using Kalman Filter which fuses the monocular ORB-SLAM outputs and navigation data measured by on-board sensors of drone. Regarding the autonomous flight and obstacle avoidance, robot needs to have a perception of its environment. To fulfill this aim, we use the surrounding map of robot which is reconstructed by monocular ORB-SLAM. But, this map is sparse and not appropriate for autonomous applications. Therefore, we represented an algorithm that lines up and enriches the reconstructed map. In the next step we determine the motion next set point and generate a collision-free path between specified set point and current robot position.

For this, a dynamic trajectory generation algorithm is proposed to fly and avoid the probable obstacles autonomously in an unknown but structured environment by utilizing some path planning methods such as potential field and RRT. The algorithm has been evaluated in real experiments and the flight variables are compared with some external precise sensors. In the experiments, it is illustrated that robot can perform reliable and robust autonomous flight in different scenarios while avoiding obstacles. Moreover, the proposed system can be easily applied to other platforms, which is being extended and implemented in our future plans.

Loop Closure Detection By Algorithmic Information Theory

Loop Closure Detection By Algorithmic Information Theory: Implemented On Range And Camera Image Data

It is assumed that a wheeled mobile robot is exploring an unknown unstructured environment, while perceiving camera or range images as its observations. These observations may be obtained with a proper sensor such as 3-D laser scanner, Lidar, Microsoft Kinect camera, stereo pairs, or monocular camera. For autonomy, it is required to avoid obstacles, perceive surrounding environment, recognize revisited places, perform path planning, mapping, and localization for a long term exploration in an unknown area or navigation toward a goal. The concentration of this paper is on loop closure detection based on the complexity of the sparse model (image model, hereafter) extracted from either camera or range images. The mobile robot position estimation becomes unreliable by closing large-scale loops due to the accumulation of estimation error. Therefore, loop closure detection approaches based on the observation similarity, which are independent from the estimated position are more accurate. A sparse model is constructed from a parametric dictionary for every range or camera image as mobile robot observations. In contrast to high-dimensional feature- based representations, in this model, the dimension of the sensor measurements’ representations is reduced. Considering the loop closure detection as a clustering problem in high- dimensional space, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms.
Exploiting the algorithmic information theory, the representation is developed such that it has the geometrically transformation invariant property in the sense of Kolmogorov complexity. A universal normalized metric is used for comparison of complexity based representations of image models. Finally, a distinctive property of normalized compression distance is exploited for detecting similar places and rejecting incorrect loop closure candidates. Experimental results show efficiency and accuracy of the proposed method in comparison to the state-of-the-art algorithms and some recently proposed methods.

Robust RGB-D SLAM

An Online Implementation of Robust RGB-D SLAM

In this project an online robust RGB-D SLAM algorithm which uses an improved switchable constraints robust pose graph slam alongside with radial variance based hash function as the loop detector. The switchable constraints robust back-end is improved by initialization of its weights according to information matrix of the loops and is validated using real world datasets. The radial variance based hash function is combined with an online image to map comparison to improve accuracy of loop detection. The whole algorithm is implemented on K. N. Toosi University mobile robot with a Microsoft Kinect camera as the RGB-D sensor and the whole algorithm is validated using this robot, while the map of the environment is generated in an online fashion. The proposed algorithm is implemented on K. N. Toosi mobile robot in a step by step implementation hierarchy, by which the importance of adding each step to the algorithm is elaborated. Graphical and numerical results are reported for each step of the extended algorithm, by which it is verified that the proposed algorithm works suitably well with RGB-D data from Kinect camera. Furthermore, it is shown that the required execution time needed for each step is such that the algorithm is promising for implementation in real time with current graphical processing unit capabilities.

 

Vision-Based Fuzzy Navigation of Mobile Robots in Grassland Environments

Vision-Based Fuzzy Navigation of Mobile Robots in Grassland Environments

Suppose a wheeled mobile robot needs to autonomously navigate in an unstructured outdoor environment using a non-calibrated regular camera as its input sensor. For safe navigation of a mobile robot in an unknown outdoor environment, we need to do the following tasks:
• Ground plane detection
• Obstacle identification
• Traversable area specification
• Navigation
We consider that the robot is navigating in a rough terrain with static obstacles, perceives the required information from a single camera and makes navigation decisions in real-time. While the robot traverses in the real world, the relative positions of the obstacles vary in the image plane and consequently the 2D projections of these points, in our case extracted features, move in some direction depending on the heading of the robot and the location of obstacle in real world. It can be seen in Fig. 1, that camera movement toward an object, increases the scale of the object in the image plane and causes apparent motion of features in the image plane. When the robot moves toward an obstacle, projected features from the obstacle move upward in the image plane if they are located above the camera’s X-Z plane. On the contrary, if the features are located below the camera’s X-Z plane, they move downward as the robot draws near the obstacle. Taking into account this property and based on the movement of features in the image plane, the robot can decide whether the corresponding 3D point is an obstacle or not, and by this way it can avoid moving toward the obstacles in the environment. Using these two properties of the apparent motion of features and a fuzzy inference system, features can be compared in relation to each other and represented by linguistic fuzzy sets, which is the base of our vision-based fuzzy navigation algorithm.

Ali Agha
Technologist at NASA-JPL, Caltech
Kasra Khosoussi
Postdoctoral Associate - LIDS, MIT
Amirhossein Tamjidi
Texas A&M University
Ehsan Mihankhah
Nanyang Technological University
Alireza Norouzzadeh Ravari
Director of Research Office, Tavanir Co
TitleAbstractYearTypePDFResearch Group
Preintegrated IMU Features For Efficient Deep Inertial Odometry
R Khorrambakht, H Damirchi, HD Taghirad
arXiv preprint arXiv:2007.02929
Abstract:

MEMS Inertial Measurement Units (IMUs) are inexpensive and effective sensors that provide proprioceptive motion measurements for many robots and consumer devices. However, their noise characteristics and manufacturing imperfections lead to complex ramifications in classical fusion pipelines. While deep learning models provide the required flexibility to model these complexities from data, they have higher computation and memory requirements, making them impractical choices for low-power and embedded applications. This paper attempts to address the mentioned conflict by proposing a computationally, efficient inertial representation for deep inertial odometry. Replacing the raw IMU data in deep Inertial models, preintegrated features improves the model's efficiency. The effectiveness of this method has been demonstrated for the task of pedestrian inertial odometry, and its efficiency has been shown through its embedded implementation on a microcontroller with restricted resources.

2020JournalPDFAutonomous Robotics
ARC-Net: Activity Recognition Through Capsules
H Damirchi, R Khorrambakht, H Taghirad
arXiv preprint arXiv:2007.03063
Abstract:

Human Activity Recognition (HAR) is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data.

2020JournalPDFAutonomous Robotics
A switched SDRE filter for state of charge estimation of lithium-ion batteries
Faraz Lotfi, Saeedeh Ziapour, Farnoosh Faraji, Hamid D. Taghirad
International Journal of Electrical Power & Energy Systems
Abstract:

Lithium-ion (Li-ion) batteries need very precise monitor of the state of charge (SOC) to ensure a long cycle life.
Hence, a knowledge of the SOC is important for Li-ion batteries. Although SOC cannot be measured directly, it can be estimated from direct measurement variables based on a model of the battery. Single-Particle-Model (SPM), a reduced-order nonlinear electrochemical model, is commonly used for this purpose. State-dependent-
Riccati-equation (SDRE) filter is chosen as the estimator due to its high-flexibility in handling the model’s nonlinearity. However, performance of this filter is limited in presence of uncertainties. To tackle this problem,
in this paper, a switching concept is induced into SDRE filter, in the form of switched estimation error covariance
matrix with a certain frequency. Thus, by changing the Riccati equation dynamic in SDRE filter and proper
adjustment of estimation error covariance matrix eigenvalues, performance and robustness of the common SDRE
filter is significantly improved for Li-ion SOC estimation. To analyze the fidelity of such a filter in further
applications, stability analysis is carried out on a class of nonlinear systems, and ultimate bound of estimation error is analytically obtained, and the influence of switching is investigated. Simulation results reveal effec-
tiveness of the proposed filter compared to common SDRE filter, extended Kalman filter and variable structure approaches. Furthermore, experimental results verify the effectiveness of the proposed method in practice.

2020JournalPDFAutonomous Robotics
System identification and H-infinity based control of quadrotor attitude
Ali Noormohammadi-Asl, Omid Esrafilian, Mojtaba Ahangar Arzati, Hamid D. Taghirad
Arxiv Optimization and Control
Abstract:

The attitude control of a quadrotor is a fundamental problem, which has a pivotal role in a
quadrotor stabilization and control. What makes this problem more challenging is the
presence of uncertainty such as unmodelled dynamics and unknown parameters. In this
paper, to cope with uncertainty, an H1 control approach is adopted for a real quadrotor.
To achieve H1 controller, first a continuous-time system identification is performed on the experimental data to encapsulate a nominal model of the system as well as a multiplicative uncertainty. By this means, H1 controllers for both roll and pitch angle are synthesized. To verify the effectiveness of the proposed controllers, some real experiments
and simulations are carried out. Results verify that the designed controller does retain robust stability, and provide a better tracking performance in comparison with a well-tuned PID and a l synthesis controller.

2019JournalPDFAutonomous Robotics
Position Estimation for Drones based on Visual SLAM and IMU in GPS-denied Environment
Hamid Didari Khamseh Motlagh, Faraz Lotfi, Saeed Bakhshi Germi, Hamid D.Taghirad
International Conference on Robotics and Mechatronics
Due to the increased rate of drone usage in various
commercial and industrial fields, the need for their autonomous
operation is rapidly increasing. One major aspect of
autonomous movement is the ability to operate safely in an
unknown environment. The majority of current works are
persistently using a global positioning system (GPS) to directly
find the absolute position of the drone. However, GPS accuracy
might be not suitable in some applications and this solution is
not applicable to all situations. In this paper, a positioning
system based on monocular SLAM and inertial measurement
unit (IMU) is presented. The position is calculated through the
semi-direct visual odometry (SVO) method alongside IMU data,
and is integrated with an extended Kalman filter (EKF) to
enhance the efficiency of the algorithm. The data is then
employed to control the drone without any requirement to any
source of external input. The experiment results for longdistance flying paths is very promising
2019ConferencePDFAutonomous Robotics
Path Planning for a UAV by Considering Motion Model Uncertainty
Hossein Sheikhi Darani, Ali Noormohammadi-Asl and Hamid D. Taghirad
International Conference on Robotics and Mechatronics
Abstract:

The primary purpose of path planning for unmanned aerial vehicles (UAVs), which is a necessary prerequisite toward an autonomous UAV, is to guide the robot to
the predefined target while the chosen path is optimized. This
paper addresses the problem of path planning for an unmanned
aerial vehicle in a 2D indoor environment, considering motion uncertainty. To cope with this challenge, the problem of
motion planning is formulated in three parts. A vision-based
extended Kalman Filter (EKF) is used to localize the UAV in
the unstructured environment. To overcome motion uncertainty,
the problem is modeled as a Markov decision problem (MDP).
Finally, a novel dynamic feedback linearization based switching
controller is proposed for point-to-point motion. Simulation and
experimental results are given to show the effectiveness of the
proposed path planning method in practice

2019ConferencePDFAutonomous Robotics
Robust Object Tracking Based on Recurrent Neural Networks
F. Lotfi, V. Ajallooeian and H. D. Taghirad
2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)
Abstract:

Object tracking through image sequences is one of the important components of many vision systems, and it has numerous applications in driver assistance systems such as pedestrian collision avoidance or collision mitigating systems. Blurred images produced by a rolling shutter camera or occlusions may easily disturb the object tracking system. In this article, a method based on convolutional and recurrent neural networks is presented to further enhance the performance and robustness of such trackers. It is proposed to use a convolutional neural network to detect an intended object and feed the tracker with found image. Moreover, by using this structure the tracker is updated every ' n ' frames. A recurrent neural network is designed to learn the object behavior for estimating and predicting its position in blurred frames or when it is occluded behind an obstacle. Real-time implementation of the proposed approach verifies its applicability for improvement of the trackers performance.

2018ConferencePDFAutonomous Robotics
Multi-goal motion planning using traveling salesman problem in belief space
Ali Noormohammadi-Asl, Hamid D. Taghirad
Information Sciences
Abstract:

In this paper, the multi-goal motion planning problem of an environment with some background information about its map is addressed in detail. The motion planning goal is to find a policy in belief space for the robot to traverse through a number of goal points. This problem is modeled as an asymmetric traveling salesman problem (TSP) in the belief space using Partially Observable Markov Decision Process (POMDP) framework. Then, feedback-based information roadmap (FIRM) algorithm is utilized to reduce the computational burden and complexity. By generating a TSP-FIRM graph, the search policy is obtained and an algorithm is proposed for online execution of the policy. Moreover, approaches to cope with challenges such as map updating, large deviations and high uncertainty in localization, which are more troublesome in a real implementation, are carefully addressed. Finally, in order to evaluate applicability and performance of the proposed algorithms, it is implemented in a simulation environment as well as on a physical robot in which some challenges such as kidnapping and discrepancies between real and computational models and map are examined.

2018JournalPDFAutonomous Robotics
Implementation of Multi-Goal Motion Planning Under Uncertainty on a Mobile Robot
Ali Noormohammadi-Asl, Hamid D. Taghirad, Amirhossein Tamjidi
2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM)
Abstract:

Multi-goal motion planning under motion and sensor uncertainty is the problem of finding a reliable policy for visiting a set of goal points. In this paper, the problem is formulated as a formidable traveling salesman problem in the belief space. To solve this intractable problem, we propose an algorithm to construct a TSP-FIRM graph which is based on the feedback-based information roadmap (FIRM) algorithm. Also, two algorithms are proposed for the online planning of the obtained policy in the offline mode and overcoming changes in the map of the environment. Finally, we apply the algorithms on a physical nonholonomic mobile robot in the presence of challenging situations like the discrepancy between the real and computation model, map updating and kidnapping.

2017ConferencePDFAutonomous Robotics
Reconstruction of B-spline curves and surfaces by adaptive group testing
Alireza Norouzzadeh Ravari, Hamid D. Taghirad
Computer-Aided Design
Abstract:

Point clouds as measurements of 3D sensors have many applications in various fields such as object modeling, environment mapping and surface representation. Storage and processing of raw point clouds is time consuming and computationally expensive. In addition, their high dimensionality shall be considered, which results in the well known curse of dimensionality. Conventional methods either apply reduction or approximation to the captured point clouds in order to make the data processing tractable. B-spline curves and surfaces can effectively represent 2D data points and 3D point clouds for most applications. Since processing all available data for B-spline curve or surface fitting is not efficient, based on the Group Testing theory an algorithm is developed that finds salient points sequentially. The B-spline curve or surface models are updated by adding a new salient point to the fitting process iteratively until the Akaike Information Criterion (AIC) is met. Also, it has been proved that the proposed method finds a unique solution so as what is defined in the group testing theory. From the experimental results the applicability and performance improvement of the proposed method in relation to some state-of-the-art B-spline curve and surface fitting methods, may be concluded.

2016JournalPDFAutonomous Robotics
NURBS-based Representation of Urban Environments for Mobile Robots
Alireza Norouzzadeh Ravari and Hamid D. Taghirad
2016 4th International Conference on Robotics and Mechatronics (ICROM)
Abstract:

Representation of the surrounding environment is a vital task for a mobile robot. Many applications for mobile robots in urban environments may be considered such as self-driving cars, delivery drones or assistive robots. In contrast to the conventional methods, in this paper a Non Uniform Rational B-Spline (NURBS) based technique is represented for 3D mapping of the surrounding environment. While in the state of the art techniques, the robot's environment is expressed in a discrete space, the proposed method is mainly developed for representation of environment in a continuous space. Exploiting the information theory, the generated representation has much lower complexity and more compression capability in relation to some state of the art techniques. In addition to representation in a lower dimensional space, the NURBS based representation is invariant against 3D geometric transformations. Furthermore, the NURBS based representation can be employed for obstacle avoidance and navigation. The applicability of the proposed algorithm is investigated in some urban environments through some publicly available data sets. It has been shown by some experiments that the proposed method has better visual representation and much better data compression compared to some state-of-the-art methods.

2016ConferencePDFAutonomous Robotics
Autonomous Flight and Obstacle Avoidance of a Quadrotor By Monocular SLAM
Omid Esrafilian and Hamid D. Taghirad
2016 4th International Conference on Robotics and Mechatronics (ICROM)
Abstract:

In this paper, a monocular vision based autonomous flight and obstacle avoidance system for a commercial quadrotor is presented. The video stream of the front camera and the navigation data measured by the drone is sent to the ground station laptop via wireless connection. Received data processed by the vision based ORB-SLAM to compute the 3D position of the robot and the environment 3D sparse map in the form of point cloud. An algorithm is proposed for enrichment of the reconstructed map, and furthermore, a Kalman Filter is used for sensor fusion. The scaling factor of the monocular slam is calculated by the linear fitting. Moreover, a PID controller is designed for 3D position control. Finally, by means of the potential field method and Rapidly exploring Random Tree (RRT) path planning algorithm, a collision-free road map is generated. Moreover, experimental verifications of the proposed algorithms are reported.

2016ConferencePDFAutonomous Robotics
A Navigation System for Autonomous Robot Operating in Unknown and Dynamic Environment: Escaping Algorithm
F. AdibYaghmaie, A. Mobarhani, H. D. Taghirad
International Journal of Robotics
Abstract:

In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation.

2016JournalPDFAutonomous Robotics
Reconstruction of B-spline curves and surfaces by adaptive group testing
Alireza Norouzzadeh Ravari, Hamid D. Taghirad
Computer-Aided Design
Abstract:

Point clouds as measurements of 3D sensors have many applications in various fields such as object modeling, environment mapping and surface representation. Storage and processing of raw point clouds is time consuming and computationally expensive. In addition, their high dimensionality shall be considered, which results in the well known curse of dimensionality. Conventional methods either apply reduction or approximation to the captured point clouds in order to make the data processing tractable. B-spline curves and surfaces can effectively represent 2D data points and 3D point clouds for most applications. Since processing all available data for B-spline curve or surface fitting is not efficient, based on the Group Testing theory an algorithm is developed that finds salient points sequentially. The B-spline curve or surface models are updated by adding a new salient point to the fitting process iteratively until the Akaike Information Criterion (AIC) is met. Also, it has been proved that the proposed method finds a unique solution so as what is defined in the group testing theory. From the experimental results the applicability and performance improvement of the proposed method in relation to some state-of-the-art B-spline curve and surface fitting methods, may be concluded.

2015JournalPDFAutonomous Robotics
Loop Closure Detection by Compressed Sensing for Exploration of Mobile Robots in Outdoor Environments
Alireza Norouzzadeh Ravari and Hamid D. Taghirad
2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM)
Abstract:

In the problem of simultaneously localization and mapping (SLAM) for a mobile robot, it is required to detect previously visited locations so the estimation error shall be reduced. Sensor observations are compared by a similarity metric to detect loops. In long term navigation or exploration, the number of observations increases and so the complexity of the loop closure detection. Several techniques are proposed in order to reduce the complexity of loop closure detection. Few algorithms have considered the loop closure detection from a subset of sensor observations. In this paper, the compressed sensing approach is exploited to detect loops from few sensor measurements. In the basic compressed sensing it is assumed that a signal has a sparse representation is a basis which means that only a few elements of the signal are non-zero. Based on the compressed sensing approach a sparse signal can be recovered from few linear noisy projections by l1 minimization. The difference matrix which is widely used for loop detection has a sparse structure, where similar observations are shown by zero distance and different locations are indicated by ones. Based on the multiple measurement vector technique which is an extension of the basic compressed sensing, the loop closure detection is performed by comparison of few sensor observations. The applicability of the proposed algorithm is investigated in some outdoor environments through some publicly available data sets. It has been shown by some experiments that the proposed method can detect loops effectively.

2015ConferencePDFAutonomous Robotics
Modified Fast-SLAM For 2D Mapping And 3D Localization
Soheil Gharatappeh, Mohammad Ghorbanian, Mehdi Keshmiri, Hamid D. Taghirad
2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM)
Abstract:

Fast Simultaneous Localization and Mapping (SLAM) algorithm is capable of real-time implementation due to logarithmic time complexity which results in decrease of computational cost. In this algorithm state vector of a robot merely includes planar location of the robot and its angle to the horizontal plane. It has fewer components comparing to state vector in extended Kalman filter method which consists of location of all environmental features. In existing methods for implementing this algorithm, robot movement is considered to be totally in planar movement; while if moving on a slope changes the pitch angle of the robot, it causes errors in the algorithm. Correcting these errors will lead to a precise 2D mapping and 3D localization. This paper details the modification added to conventional Fast-Slam algorithm to accommodate this requirement by using an IMU. Simulation and experimental results shows the effectiveness of such modification.

2015ConferencePDFAutonomous Robotics
3D Scene and Object Classification Based on Information Complexity of Depth Data
A. Norouzzadeh, H. D. Taghirad
Mathematics
Abstract:

In this paper the problem of 3D scene and object classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new definition for the Kolmogorov complexity is presented based on the Earth Mover’s Distance (EMD). Finally the classification of 3D scenes and objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D object classification methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall.

2015JournalPDFAutonomous Robotics
Transformation Invariant 3D Object Recognition Based On Information Complexity
Alireza Norouzzadeh Ravari and Hamid D. Taghirad
2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)
Abstract:

The 3D representation of objects and scenes as a point cloud or range image has been made simple by means of sensors such as Microsoft Kinect, stereo camera or laser scanner. Various tasks, such as recognition, modeling and classification can not be performed on raw measurements because of the curse of high dimensionality, computational and algorithm complexity. Non Uniform Rational Basis Splines (NURBS) are a widely used representation technique for 3D objects in various robotics and Computer Aided Design (CAD) applications. In this paper, a similarity measurement from information theory is employed in order to recognize an object sample from a set of objects. From a NURBS model fitted to the observed point cloud, a complexity based representation is derived which is transformation invariant in the sense of Kolmogorov complexity. Experimental results on a set of 3D objects grabbed by a Kinect sensor indicates the applicability of the proposed method for object recognition tasks. Furthermore, the results of the proposed method is compared to that of some state of the art algorithms.

2014ConferencePDFAutonomous Robotics
An Online Implementation of Robust RGB-D SLAM
M. A. Athari, H. D. Taghirad
2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)
Abstract:

This paper presents an online robust RGB-D SLAM algorithm which uses an improved switchable constraints robust pose graph slam alongside with radial variance based hash function as the loop detector. The switchable constraints robust back-end is improved by initialization of its weights according to information matrix of the loops and is validated using real world datasets. The radial variance based hash function is combined with an online image to map comparison to improve accuracy of loop detection. The whole algorithm is implemented on K. N. Toosi University mobile robot with a Microsoft Kinect camera as the RGB-D sensor and the whole algorithm is validated using this robot, while the map of the environment is generated in an online fashion.

2014ConferencePDFAutonomous Robotics
An Improved Optimization Method for iSAM2
Rana Talaei Shahir and Hamid D. Taghirad
2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)
Abstract:

There is an issue called maximum likelihood estimation in SLAM that corresponds to a nonlinear least-square problem. It is expected to earn an accurate solution for large-scale environments with high speed of convergence. Although all the applied optimization methods might be accepted in terms of accuracy and speed of convergence for small datasets, their solutions for large-scale datasets are often far from the ground truth. In this paper, a double Dogleg trust region method is proposed and adjusted with iSAM2 to level up performance and accuracy of the algorithm especially in large-scale datasets. Since the trust region methods are sensitive to their own parameters, Gould parameters are chosen to obtain better performance. Simulations are done on some large-scale datasets and the results indicate that the proposed method is more efficient compared to the conventional iSAM2 algorithm.

2014ConferencePDFAutonomous Robotics
Loop Closure Detection By Algorithmic Information Theory: Implemented On Range And Camera Image Data
Alireza Norouzzadeh Ravari and Hamid D. Taghirad
IEEE Transactions on Cybernetics
Abstract:

In this paper the problem of loop closing from depth or camera image information in an unknown environment is investigated. A sparse model is constructed from a parametric dictionary for every range or camera image as mobile robot observations. In contrast to high-dimensional feature-based representations, in this model, the dimension of the sensor measurements' representations is reduced. Considering the loop closure detection as a clustering problem in high-dimensional space, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In this paper, a representation is developed from a sparse model of images, with a lower dimension than original sensor observations. Exploiting the algorithmic information theory, the representation is developed such that it has the geometrically transformation invariant property in the sense of Kolmogorov complexity. A universal normalized metric is used for comparison of complexity based representations of image models. Finally, a distinctive property of normalized compression distance is exploited for detecting similar places and rejecting incorrect loop closure candidates. Experimental results show efficiency and accuracy of the proposed method in comparison to the state-of-the-art algorithms and some recently proposed methods.

2014JournalPDFAutonomous Robotics
An intelligent UFastSLAM with MCMC move step
Ramazan Havangi, Mohammad Ali Nekoui, Hamid D. Taghirad and Mohammad Teshnehlab
Advanced Robotics
Abstract:

FastSLAM is a framework for simultaneous localization and mapping (SLAM). However, FastSLAM algorithm has two serious drawbacks, namely the linear approximation of nonlinear functions and the derivation of the Jacobian matrices. For solving these problems, UFastSLAM has been recently proposed. However, UFastSLAM is inconsistent over time due to the loss of particle diversity that is caused mainly by the particle depletion in the resampling step and incorrect a priori knowledge of process and measurement noises. To improve consistency, intelligent UFastSLAM with Markov chain Monte Carlo (MCMC) move step is proposed. In the proposed method, the adaptive neuro-fuzzy inference system supervises the performance of UFastSLAM. Furthermore, the particle impoverishment caused by resampling is restrained after the resample step with MCMC move step. Simulations and experiments are presented to evaluate the performance of algorithm in comparison with UFastSLAM. The results show the effectiveness of the proposed method.

2013JournalPDFAutonomous Robotics
Unsupervised 3D Object Classification from Range Image Data by Algorithmic Information Theory
Alireza Norouzzadeh Ravari and Hamid D. Taghirad
2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)
Abstract:

The problem of unsupervised classification of 3D objects from depth information is investigated in this paper. The range images are represented efficiently as sensor observations. Considering the high-dimensionality of 3D object classification, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In order to remedy this problem, a low-dimensional representation is defined here. The sparse model of every range image is constructed from a parametric dictionary. Employing the algorithmic information theory, a universal normalized metric is used for comparison of Kolmogorov complexity based representations of sparse models. Finally, most similar objects are grouped together. Experimental results show efficiency and accuracy of the proposed method in comparison to a recently proposed method.

2013ConferencePDFAutonomous Robotics
A New Method for Mobile Robot Navigation in Dynamic Environment: Escaping Algorithm
F. Adib Yaghmaie, A. Mobarhani and H. D. Taghirad
Robotics and Mechatronics (ICRoM)
Abstract:

This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation.

2013ConferencePDFAutonomous Robotics
A New Method for Mobile Robot Navigation in Dynamic Environment: Escaping Algorithm
Farnaz Adib Yaghmaie, Amir Mobarhani, and Hamid D. Taghirad
Robotics and Mechatronics
Abstract:

This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation.

2013ConferencePDFAutonomous Robotics
Feedback Error learning Control of Trajectory Tracking of Non-Holonomic Mobile Robot
Farnaz Adib Yaghmaie, Fateme Bakhshande and Hamid D.Taghirad
20th Iranian Conference on Electrical Engineering
Abstract:

In this paper a new controller for nonholonomic system is introduced. This feedback error learning controller benefits from both nonlinear and adaptive controller properties. The nonlinear controller is used to stabilize the nonholonomic behavior of the systems. This controller is a sliding mode controller which is designed based on backstepping method. The adaptive controller tries to face with uncertainty and unknown dynamic of the mobile robot. This part uses neural network controller for adaptation. The experimental results show the effectiveness of proposed controller and suitable and robust tracking performance of a mobile robot, which is significantly better than traditional controllers.

2012ConferencePDFAutonomous Robotics
Stereo-Based Visual Navigation of Mobile Robots in Unknown Environments
H. Soltani, H. D. Taghirad and A.R. Norouzzadeh Ravari
20th Iranian Conference on Electrical Engineering (ICEE2012)
Abstract:

In this paper a stereo vision-based algorithm for mobile robots navigation and exploration in unknown outdoor environments is proposed. The algorithm is solely based on stereo images and implemented on a nonholonomic mobile robot. The first step for exploration in unknown environments is construction of the map of circumference in real-time. By getting disparity image from rectified stereo images and translating its data to 3D-space, point cloud model of environments is constructed. Then by projecting points to XZ plane and put local maps together based on visual odometry, global map of environment is constructed in real-time. A* algorithm is used for investigating optimal path and nonlinear back-stepping controller guides the robot to follow the identified path. Finally, the mobile robot explores for a desired object in an unknown environment through these steps. Experimental results verify the effectiveness of the proposed algorithm in real-time implementations.

2012ConferencePDFAutonomous Robotics
Histogram Based Frontier Exploration
Amir Mobarhani, Shaghayegh Nazari, Amir H. Tamjidi, Hamid D. Taghirad
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abstract:

This paper proposes a method for mobile robot exploration based on the idea of frontier exploration which suggests navigating the robot toward the boundaries between free and unknown areas in the map. A global occupancy grid map of the environment is constantly updated, based on which a global frontier map is calculated. Then, a histogram based approach is adopted to cluster frontier cells and score these clusters based on their distance from the robot as well as the number of frontier cells they contain. In each stage of the algorithm, a sub-goal is set for the robot to navigate. A combination of distance transform and A* search algorithms is utilized to generate a plausible path toward the sub-goal through the free space. This way keeping a reliable distance from obstacles is guaranteed while searching for the shortest path toward the sub-goal. When such a path is generated, a B-spline interpolated and smoothed trajectory is produced as the control reference for the mobile robot to follow. The whole process is iterated until no unexplored area remains in the map. The efficiency of the method is shown through simulated and real experiments.

2011ConferencePDFAutonomous Robotics
Vision-Based Fuzzy Navigation of Mobile Robots in Grassland Environments
A. R. Norouzzadeh Ravari, H. D. Taghirad, A. H. Tamjidi
Advanced Intelligent Mechatronics
Abstract:

In this paper a vision-based algorithm for mobile robot navigation in unknown outdoor environments is proposed. It is based on a simple phenomenon, that when the robot moves forward, projected images of the near obstacles grow in captured frames faster than that of the far objects. The proposed algorithm takes advantage of this property and extracts features from each grabbed frame of the camera and tracks the vertical position of the features and their speed along the Y axis of the image plane over multiple frames as the robot moves. The relative height of the features and their distance from the robot in 3D is inferred based on this data and they are fed into a fuzzy reasoning system which marks the features from safe to unsafe according to their suitability for navigation. Then a second fuzzy system summarizes these scores in different image regions and directs the robot toward the area containing more features marked as safe. Simulation and implementation results confirm the efficacy of the proposed simple algorithm for mobile robot navigation in outdoor environments.

2009ConferencePDFAutonomous Robotics
Line Matching Localization and Map Building with Least Square
E. Mihankhah, H.D. Taghirad, A. Kalantari, E. Aboosaeedan, H. Semsarilar
2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Abstract:

We introduce a very fast and robust localization and 2D environment representation algorithm in this paper. This innovative method matches lines extracted from the LASER range finder distance data with the lines that construct the map, in order to calculate the local translation and rotation. This matching is done with a simple least square with no iterations. The algorithm is suitable for any indoor environment with mostly polygonal structure and has proven high speed and robustness in the experimental tests on our innovatively designed tracked mobile rescue robot ldquoSilverrdquo. One experimental test is presented in the last section of this paper where the outputs are presented. These outputs are: 1-drift free raster map made of points and 2- A gallery of lines providing a linear ground truth.

2009ConferencePDFAutonomous Robotics
A novel hybrid Fuzzy-PID controller for tracking control of robot manipulators
A. R. Norouzzadeh Ravari, H.D. Taghirad
2008 IEEE International Conference on Robotics and Biomimetics
Abstract:

In this paper, a novel hybrid fuzzy proportional-integral-derivative (PID) controller based on learning automata for optimal tracking of robot systems including motor dynamics is presented. Learning automata is used at the supervisory level for adjustment of the parameters of hybrid Fuzzy-PID controller during the system operation. The proposed method has better convergence rate in comparison with standard back-propagation algorithms, less computational requirements than adaptive network based fuzzy inference systems (ANFIS) or neural based controllers and having the ability of working in uncertain environments without any previous knowledge of environments' parameters. The proposed controller has been successfully applied in simulation to control a 6-DOF Puma 560 manipulator using robotic toolbox, and has satisfactory results. In this simulation also, external disturbance and noise are addressed. The result of simulation has also shown that the rate of convergence and robustness of the designed controller guarantees practical stability.

2009ConferencePDFAutonomous Robotics
Autonomous Staircase Detection and Stair Climbing for a Tracked Mobile Robot using Fuzzy Controller
E. Mihankhah, A. Kalantari, E. Aboosaeedan, H.D. Taghirad, and S.Ali.A. Moosavian
2008 IEEE International Conference on Robotics and Biomimetics
Abstract:

Theoretical analysis and implementation of autonomous staircase detection and stair climbing algorithms on a novel rescue mobile robot are presented in this paper. The main goals are to find the staircase during navigation and to implement a fast, safe and smooth autonomous stair climbing algorithm. Silver is used here as the experimental platform. This tracked mobile robot is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas. Its performance has been demonstrated in rescue robot league of international RoboCup competitions. A fuzzy controller is applied to direct the robot during stair climbing. Controller inputs are generated by processing the range data from two laser range finders which scan the environment one horizontally and the other vertically. The experimental results of stair detection algorithm and stair climbing controller are demonstrated at the end.

2009ConferencePDFAutonomous Robotics
SLAM Using Single Laser Range Finder
AliAkbar Aghamohammadi, Amir H. Tamjidi, Hamid D. Taghirad
IFAC Proceedings Volumes
Abstract:

Presented method in this paper aims to develop an accurate motion model and SLAM algorithm, which is only based on the Laser Range Finder (LRF) data. Proposed method tries to overcome some practical problems in traditional motion models and SLAM approaches, such as robot slippage, and inaccuracy in parameters related to robot's hardware. Novel insights specific to process and measurement model, and making use of them in the IEKF framework, give rise to the real time method with drift-free performance in restricted environments. Furthermore, uncertainty measures, calculated through the method, are valuable information for fusion purposes and also an accurate motion model, derived in this method, can be used as a robust and an accurate localization procedure in different structured environments. These issues are validated through experimental implementations; experiments verify method's efficiency both in pure localization and in SLAM scenarios in the restricted environments, involving loop closures.

2008ConferencePDFAutonomous Robotics
A Solution for SLAM through Augmenting Vision and Range Information
Ali A. Aghamohammadi, Amir H. Tamjidi, Hamid D. Taghirad
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abstract:

This paper proposes a method for augmenting the information of a monocular camera and a range finder. This method is a valuable step towards solving the SLAM problem in unstructured environments free from problems of using encoderspsila data. Proposed algorithm causes the robot to benefit from a feature-based map for filtering purposes, while it exploits an accurate motion model, based on point-wise raw range scan matching rather than unreliable feature-based range scan matching, in unstructured environments. Moreover, robust loop closure detection procedure is the other consequence of this method. Experiments with a low-cost IEEE 1394 webcam and a range finder illustrate the effectiveness of the proposed method in drift-free SLAM at loop closing motions in unstructured environments.

2008ConferencePDFAutonomous Robotics
Feature-Based Laser Scan Matching For Accurate and High Speed Mobile Robot Localization
A. A. Aghamohammadi, H. D. Taghirad, A. H. Tamjidi, and E. Mihankhah
Proceedings of the 3rd European Conference on Mobile Robots
Abstract:

This paper introduces an accurate and high speed pose tracking method for mobile robots based on matching of extracted features from consecutive scans. The feature extraction algorithm proposed in this paper uses a global information of the whole scan data and local information around feature points. Uncertainty of each feature is represented using covariance matrices determined due to observation and quantization error. Taking into account each feature's uncertainty in pose shift calculation leads to an accurate estimation of robot pose. Experiments with low range URG_X002 laser range scanner illustrate the effectiveness of the proposed method for mobile robot localization.

2007ConferencePDFAutonomous Robotics
New Wavelet Based Algorithm for Real Time Visual Tracking
Akram Bayat, Hamid R. Taghirad, Seyyed Sadegh Mottaghian
Abstract:

in this paper, we propose a new technique in wavelet domain for real time object detection and tracking in a sequence of images. The object to be tracked is identified in the first frame. Our proposed algorithm consists of two phases: the first, wavelet based edge detection is used to form ground boundary map. Then, Object dimensions estimation is implemented to determine probabilistic object areas. finally, target detection based on finding best match using feature vectors is applied. We defined dispersion of wavelet detail coefficient in object area as feature to be matched. Also we proposed a new color model for images to be used in processing algorithm. Our experimental results show that the algorithm is robust and fast. It is also insensitive to changing illumination condition and size of target

2006ConferencePDFAutonomous Robotics
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