RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots
RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots
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DOI：
10.3390/s19102251
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发表时间：
2019-05-02
期刊：
影响因子：
3.9
通讯作者：
Schwertfeger, Soren
中科院分区：
文献类型：
--
作者：
Shan, Zeyong;Li, Ruijian;Schwertfeger, Soren
利用摄像机传感器进行地面机器人同时定位与地图构建（SLAM）与基于激光的方法相比具有许多优点，例如低成本和更高的鲁棒性。RGBD传感器承诺两全其美：来自相机的具有深度信息的密集数据。本文提出了融合RGBD和IMU数据的视觉SLAM系统，称为VINS-RGBD，这是建立在开源的VINS-Mono软件。本文分析了VINS方法，并强调了可观测性问题。然后，我们扩展VINS单声道系统，使深度数据在初始化过程中，以及在VIO（视觉惯性里程计）阶段的使用。此外，我们整合了一个映射系统的基础上，二次采样的深度数据和八叉树滤波，以实现实时映射，包括循环关闭。我们提供软件和数据集进行评估。我们进行了广泛的实验，手持，轮式和履带式机器人在不同的环境中。我们表明，ORB-SLAM 2失败，我们的应用程序，看到我们的VINS-RGBD方法是上级VINS-Mono。
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.