A robust data association for simultaneous localization and mapping in dynamic environments

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.

AN IMPROVED SIMULTANEOUS LOCALIZATION AND MAPPING FOR DYNAMIC ENVIRONMENTS

International Journal of Robotics and Automation ◽

10.2316/j.2021.206-0404 ◽

2021 ◽

Vol 36 (0) ◽

Author(s):

S. Badalkhani ◽

R. Havangi ◽

M. Farshad

Keyword(s):

Dynamic Environments ◽

Simultaneous Localization and Mapping for Mobile Robots in Dynamic Environments

2013 International Conference on Information Science and Applications (ICISA) ◽

10.1109/icisa.2013.6579440 ◽

2013 ◽

Cited By ~ 4

Author(s):

Seungwon Oh ◽

Minsoo Hahn ◽

Jinsul Kim

Keyword(s):

Mobile Robots ◽

Dynamic Environments ◽

Data Association Analysis in Simultaneous Localization and Mapping Problem

International Journal of Integrated Engineering ◽

10.30880/ijie.2019.11.04.012 ◽

2019 ◽

Vol 11 (4) ◽

Author(s):

Hamzah Ahmad ◽

◽

Nur Aqilah Othman ◽

Mohd Mawardi Saari ◽

Mohd Syakirin Ramli ◽

...

Keyword(s):

Association Analysis ◽

Data Association ◽

Mapping Problem ◽

Data association for simultaneous localization and mapping in robotic wireless sensor networks

2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics ◽

10.1109/aim.2010.5695906 ◽

2010 ◽

Cited By ~ 5

Author(s):

Rex Wong ◽

Jizhong Xiao ◽

Samleo L. Joseph ◽

Zeyong Shan

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Association ◽

Wireless Sensor ◽

Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

Cybernetics and Information Technologies ◽

10.2478/cait-2014-0035 ◽

2014 ◽

Vol 14 (3) ◽

pp. 86-95 ◽

Cited By ~ 1

Author(s):

Yingmin Yi ◽

Ying Huang

Keyword(s):

Correlation Function ◽

Graph Theory ◽

Sequence Data ◽

Data Association ◽

Geometric Information ◽

Maximum Correlation ◽

Noise Uncertainty ◽

Abstract The paper proposes landmark sequence data association for Simultaneous Localization and Mapping (SLAM) for data association problem under conditions of noise uncertainty increase. According to the space geometric information of the environment landmarks, the information correlations between the landmarks are constructed based on the graph theory. By observing the variations of the innovation covariance using the landmarks of the adjacent two steps, the problem is converted to solve the landmark TSP problem and the maximum correlation function of the landmark sequences, thus the data association of the observation landmarks is established. Finally, the experiments prove that our approach ensures the consistency of SLAM under conditions of noise uncertainty increase.

Lidar-based Simultaneous Localization and Mapping in Dynamic Environments

10.1109/cvci54083.2021.9661215 ◽

2021 ◽

Author(s):

Binbin Feng ◽

Chunyun Fu ◽

Lizhou Liao ◽

Yun Zhu

Keyword(s):

Dynamic Environments ◽

Simultaneous localization and mapping: A feature-based probabilistic approach

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-009-0045-z ◽

2009 ◽

Vol 19 (4) ◽

pp. 575-588 ◽

Cited By ~ 24

Author(s):

Piotr Skrzypczyński

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Data Association ◽

Probabilistic Approach ◽

Probabilistic Formulation ◽

Recent Advances ◽

Localization And Mapping ◽

Feature Based ◽

Points Of View

Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.