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RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

1
School of Information Science & Technology, ShanghaiTech University, Shanghai 201210, China
2
Chinese Academy of Sciences, Shanghai Institute of Microsyst & Information Technology, Shanghai 200050, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(10), 2251; https://doi.org/10.3390/s19102251
Received: 2 April 2019 / Revised: 5 May 2019 / Accepted: 7 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Inertial Sensors)
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Abstract

Using camera sensors for ground robot Simultaneous Localization and Mapping (SLAM) has many benefits over laser-based approaches, such as the low cost and higher robustness. RGBD sensors promise the best of both worlds: dense data from cameras with depth information. This paper proposes to fuse RGBD and IMU data for a visual SLAM system, called VINS-RGBD, that is built upon the open source VINS-Mono software. The paper analyses the VINS approach and highlights the observability problems. Then, we extend the VINS-Mono system to make use of the depth data during the initialization process as well as during the VIO (Visual Inertial Odometry) phase. Furthermore, we integrate a mapping system based on subsampled depth data and octree filtering to achieve real-time mapping, including loop closing. We provide the software as well as datasets for evaluation. Our extensive experiments are performed with hand-held, wheeled and tracked robots in different environments. We show that ORB-SLAM2 fails for our application and see that our VINS-RGBD approach is superior to VINS-Mono.
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Keywords: visual-inertial systems; SLAM; inertial motion tracking; ground robots; rescue robots; sensor fusion; state estimation; RGBD sensor visual-inertial systems; SLAM; inertial motion tracking; ground robots; rescue robots; sensor fusion; state estimation; RGBD sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shan, Z.; Li, R.; Schwertfeger, S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors 2019, 19, 2251.

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