Hu Bo, Luo Jingwen
School of Information Science and Technology, Yunnan Normal University, No. 768 Juxian Street, Chenggong District, Kunming 650500, China.
Biomimetics (Basel). 2023 Aug 16;8(4):371. doi: 10.3390/biomimetics8040371.
Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot's pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot's motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot's pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot's pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm.
动态物体在动态场景下的移动机器人位姿估计中会带来大量误差累积，导致无法构建与周围环境一致的地图。基于此，本文提出了一种基于稠密光流的移动机器人鲁棒半直接三维同步定位与建图（SLAM）算法。首先，使用稀疏直接法对机器人位姿进行初步估计，并利用单应性矩阵对当前帧图像进行补偿，以减少机器人运动过程中旋转引起的图像变形。然后，通过计算相邻两帧的稠密光流场，并基于动态阈值对场景中的动态区域进行分割，消除投影在动态区域内的局部地图点。在此基础上，通过最小化重投影误差对机器人位姿进行优化。此外，制定了一种高性能关键帧选择策略，在机器人位姿成功跟踪时插入关键帧。同时，提取特征点并与关键帧进行匹配，用于后续的优化和建图。考虑到直接法在实际应用场景中容易出现跟踪失败的情况，在机器人重定位时采用关键帧的特征点和地图点。最后，将所有关键帧和地图点作为优化变量进行全局束调整（BA）优化，以构建全局一致的三维稠密八叉树地图。一系列仿真和实验证明了所提算法的优越性能。
