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RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

School of Information Science & Technology, ShanghaiTech University, Shanghai 201210, China
Chinese Academy of Sciences, Shanghai Institute of Microsyst & Information Technology, Shanghai 200050, China
University of Chinese Academy of Sciences, Beijing 100049, China
Authors to whom correspondence should be addressed.
Sensors 2019, 19(10), 2251;
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)
PDF [32554 KB, uploaded 15 May 2019]


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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Shan, Z.; Li, R.; Schwertfeger, S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors 2019, 19, 2251.

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