scholarly journals Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

2005 ◽  
Vol 19 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Denis F. Wolf ◽  
Gaurav S. Sukhatme
Keyword(s):  
Mobile Robot ◽  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Localization And Mapping

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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.

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