RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots
AbstractUsing 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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Shan, Z.; Li, R.; Schwertfeger, S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors 2019, 19, 2251.
Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors. 2019; 19(10):2251.Chicago/Turabian Style
Shan, Zeyong; Li, Ruijian; Schwertfeger, Sören. 2019. "RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots." Sensors 19, no. 10: 2251.
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