Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

2012 ◽  
Vol 28 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Fernando A. Auat Cheein ◽  
Fernando M. Lobo Pereira ◽  
Fernando di Sciascio ◽  
Ricardo Carelli
Keyword(s):  
Monte Carlo ◽  
Mobile Robot ◽  
Simultaneous Localization And Mapping ◽  
Path Following ◽  
Monte Carlo Experiment ◽  
Localization And Mapping ◽  
Trajectory Following ◽  
Uncertainty Map ◽  
Slam Algorithm ◽  
Representative Points

AbstractThis paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

Towards features updating selection based on the covariance matrix of the SLAM system state

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 271-282 ◽  
Author(s):  
Fernando A. Auat Cheein ◽  
Fernando di Sciascio ◽  
Gustavo Scaglia ◽  
Ricardo Carelli
Keyword(s):  
Mobile Robot ◽  
Selection Criterion ◽  
Selection Procedure ◽  
Mobile Robot Navigation ◽  
Monte Carlo Experiment ◽  
Features Selection ◽  
System State ◽  
Localization And Mapping ◽  
Leibler Divergence ◽  
Slam Algorithm

SUMMARYThis paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback–Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work.

scholarly journals Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Xuewu Zhang ◽  
Dongmin Choi
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Absolute Measurement ◽  
Relative Measurement ◽  
Localization Algorithm ◽  
Moving Vehicle ◽  
Localization And Mapping ◽  
Slam Algorithm

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

scholarly journals Multi-Focal Vision and Gaze Control Improve Navigation Performance

10.5772/50920 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 25 ◽  
Author(s):  
Kolja Kühnlenz ◽  
Martin Buss
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Active Vision ◽  
Path Following ◽  
Mobile Robot Navigation ◽  
Vision Systems ◽  
Localization Accuracy ◽  
Localization And Mapping ◽  
Novel Concept ◽  
The Impact

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

scholarly journals Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772671
Author(s):  
Jiuqing Wan ◽  
Shaocong Bu ◽  
Jinsong Yu ◽  
Liping Zhong
Keyword(s):  
Mobile Robot ◽  
Posterior Probability ◽  
Belief Propagation ◽  
Nonlinear Models ◽  
Simultaneous Localization And Mapping ◽  
Factor Graph ◽  
Posterior Probability Distribution ◽  
Localization And Mapping ◽  
Nonparametric Techniques ◽  
Non Gaussian

This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

scholarly journals An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

2018 ◽  
Vol 28 (3) ◽  
pp. 505-519
Author(s):  
Demeng Li ◽  
Jihong Zhua ◽  
Benlian Xu ◽  
Mingli Lu ◽  
Mingyue Li
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Posterior Density ◽  
Operation System ◽  
Feature Map ◽  
Localization And Mapping ◽  
Finite Set ◽  
Feature Based ◽  
Vehicle Trajectory ◽  
Slam Algorithm ◽  
Random Finite Sets

Abstract Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

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