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Article

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

by 1,2, 3,*, 4, 1 and 1,2
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China
2
Shenzhen Jimuyida Technology Co., Ltd., Shenzhen 518000, China
3
School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China
4
School of Resource and Environment Sciences, Wuhan University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040202
Received: 25 February 2020 / Revised: 18 March 2020 / Accepted: 23 March 2020 / Published: 27 March 2020
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments. View Full-Text
Keywords: visual SLAM; deep learning; dynamic scenes; Mask R-CNN; optical flow; ORB-SLAM2 visual SLAM; deep learning; dynamic scenes; Mask R-CNN; optical flow; ORB-SLAM2
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MDPI and ACS Style

Cheng, J.; Wang, Z.; Zhou, H.; Li, L.; Yao, J. DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes. ISPRS Int. J. Geo-Inf. 2020, 9, 202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040202

AMA Style

Cheng J, Wang Z, Zhou H, Li L, Yao J. DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes. ISPRS International Journal of Geo-Information. 2020; 9(4):202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040202

Chicago/Turabian Style

Cheng, Junhao, Zhi Wang, Hongyan Zhou, Li Li, and Jian Yao. 2020. "DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes" ISPRS International Journal of Geo-Information 9, no. 4: 202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040202

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