Data association using relative compatibility of multiple observations for EKF-SLAM

Correct data association is crucial to perform self-localization and map building for mobile robot. The nearest neighbor method based on the maximum likelihood is widely used. However, this algorithm has two problems, possibility of false association and spurious association. These problems happen m...

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Bibliographic Details
Published in:Intelligent service robotics 2016-07, Vol.9 (3), p.177-185
Main Authors: Choi, Jinwoo, Choi, Minyong, Chung, Wan Kyun, Choi, Hyun-taek
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Candidates
Compatibility
Control
Dynamical Systems
Engineering
Error correction
Hypotheses
Mechatronics
Original Research Paper
Robotics
Robotics and Automation
Simultaneous localization and mapping
User Interfaces and Human Computer Interaction
Vibration
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Summary:Correct data association is crucial to perform self-localization and map building for mobile robot. The nearest neighbor method based on the maximum likelihood is widely used. However, this algorithm has two problems, possibility of false association and spurious association. These problems happen more severely when the vehicle pose error is large and the covariance does not represent the uncertainty correctly. In this paper, a data association method which applies the concept of pairwise relative compatibility to the probabilistic data association problem is proposed. The proposed method handles the false and spurious association problems effectively. We prove its performance by the EKF-SLAM simulations and experiments and the results show that the proposed data association provides reliable data association.
ISSN:1861-2776
1861-2784
DOI:10.1007/s11370-016-0200-y