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JRM Vol.33 No.6 pp. 1385-1397
doi: 10.20965/jrm.2021.p1385
(2021)

Paper:

Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

Leyuan Sun*,**,***, Rohan P. Singh*,**,***, and Fumio Kanehiro*,**,***

*Department of Intelligent and Mechanical Interaction Systems, Graduate School of Science and Technology, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577 Japan

**CNRS-AIST JRL (Joint Robotics Laboratory), International Research Laboratory (IRL)
1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan

***National Institute of Advanced Industrial Science and Technology (AIST)
1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan

Received:
March 12, 2021
Accepted:
June 18, 2021
Published:
December 20, 2021
Keywords:
visual SLAM, dynamic environment, loop closure detection
Abstract
Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

CNN-based LCD in dynamic environment

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

Cite this article as:
L. Sun, R. Singh, and F. Kanehiro, “Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments,” J. Robot. Mechatron., Vol.33, No.6, pp. 1385-1397, 2021.
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