Abstract: The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.

Key words: computer vision, Simultaneous Localization And Mapping (SLAM), autonomous mobile robot, octree map, obstacle avoidance

摘要： 移动机器人在探索未知环境且没有外部参考系统的情况下，面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题，提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先，根据关键帧的位姿和深度数据，构建图像对应场景的点云地图；然后利用八叉树地图技术进行处理，构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验，实验结果表明，所提算法具有较高的有效性、精度和鲁棒性。最后，搭建了自主移动机器人，将改进的VSLAM系统应用到移动机器人中，能够实时地完成自主避障和三维地图构建，解决稀疏地图无法用于避障和导航的问题。

关键词: 计算机视觉, 同时定位与地图构建, 自主移动机器人, 八叉树地图, 避障