室内动态环境下基于网格分割与双地图耦合的RGB-D SLAM算法

doi:10.13973/j.cnki.robot.210224

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艾青林, 王威, 刘刚江. 室内动态环境下基于网格分割与双地图耦合的RGB-D SLAM算法[J]. 机器人, 2022, 44(4): 431-442.DOI: 10.13973/j.cnki.robot.210224. AI Qinglin, WANG Wei, LIU Gangjiang. RGB-D SLAM Algorithm in Indoor Dynamic Environments Based on Gridding Segmentation and Dual Map Coupling. ROBOT, 2022, 44(4): 431-442. DOI: 10.13973/j.cnki.robot.210224.

摘要为解决室内动态环境下现有RGB-D SLAM（同步定位与地图创建）系统定位精度低、建图效果差的问题，提出一种基于网格分割与双地图耦合的RGB-D SLAM算法。基于单应运动补偿与双向补偿光流法，根据几何连通性与深度图像聚类结果实现网格化运动分割，同时保证算法的快速性。利用静态区域内的特征点最小化重投影误差对相机进行位置估计。结合相机位姿、RGB-D图像、网格化运动分割图像，同时构建场景的稀疏点云地图和静态八叉树地图并进行耦合，在关键帧上使用基于网格分割和八叉树地图光线遍历的方法筛选静态地图点，更新稀疏点云地图，保障定位精度。公开数据集和实际动态场景中的实验结果都表明，本文算法能够有效提升室内动态场景中的相机位姿估计精度，实现场景静态八叉树地图的实时构建和更新。此外，本文算法能够实时运行在标准CPU硬件平台上，无需GPU等额外计算资源。

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	艾青林
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关键词 ：动态环境, 同时定位与地图构建, RGB-D, 光流法, 八叉树地图

Abstract：To deal with low pose estimation accuracy and poor mapping performance of existing RGB-D SLAM (simultaneous localization and mapping) systems in indoor dynamic environments, an RGB-D SLAM algorithm with gridding segmentation and dual map coupling is proposed. Based on homography motion compensation and bidirectionally compensated optical flow method, gridding motion segmentation is achieved according to the geometrical connectivity and clustering result on depth images. Meanwhile, the speed of the algorithm in the segmentation process is ensured. Only features in the static region are used to estimate the camera position by minimizing the reprojection error. By combining camera poses, RGB-D images, and gridding motion segmentation images, the sparse point cloud map and the static octree map of the scene are constructed simultaneously and coupled. The static map points are filtered out with the gridding segmentation method and the octree map based ray traversal method on the keyframes, and thus the sparse point cloud map is updated to ensure the positioning accuracy. Experimental results on public datasets and in actual dynamic scenes show that the proposed method can effectively improve the accuracy of camera pose estimation in indoor dynamic scenes and achieve the real-time construction and update for static octree maps of scenes. Moreover, the proposed method can run in real-time on common CPU hardware platforms without additional computational resources, such as GPU.

Key words： dynamic environment simultaneous localization and mapping RGB-D optical flow octree map

收稿日期: 2021-07-30 录用日期: 2022-03-08

P242.6

基金资助:国家自然科学基金（52075488）；浙江省自然科学基金（LY20E050023）.

通讯作者: 艾青林，aqlaql@163.com E-mail: aqlaql@163.com

作者简介: 艾青林（1976-），男，博士，教授。研究领域：智能移动机器人技术。
王威（1997-），男，硕士生。研究领域：移动机器人，视觉SLAM。
刘刚江（1995-），男，硕士生。研究领域：移动机器人，视觉SLAM。

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.210224 或 https://robot.sia.cn/CN/Y2022/V44/I4/431

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