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.
Highlights
With the sensor and algorithm innovations [1], mobile robots are getting smaller and smarter and are addressing new applications in medicine, agriculture, and security applications [2,3]
The goal of our work is to enable a small rescue robot to do visual Simultaneous Localization and Mapping (SLAM) and navigation
We proposed a system to fuse color, depth and inertial sensors for trajectory estimation and mapping for small ground rescue robots
Summary
With the sensor and algorithm innovations [1], mobile robots are getting smaller and smarter and are addressing new applications in medicine, agriculture, and security applications [2,3]. Search and rescue robots are usually equipped with LIDAR and high-quality IMU sensors, which are fused with the wheel odometry to estimate the poses and the map [7,8,9]. These leads to expensive robots, which is undesirable, because such mobile rescue robots have a high risk of breaking or being lost during operation. Many methods have been proposed to improve the accuracy, robustness, and efficiency of vSLAM, such as using first estimation Jacobian [11] to reduce the inherent nonlinearity in the system. Monocular cameras were combined with other sensors such as depth cameras [13,14] and IMU [15,16,17] to achieve more robust performance
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