动态环境下基于语义信息与几何约束的视觉SLAM系统

Visual SLAM based on semantic information and geometric constraints in dynamic environment

目前同时定位与地图构建技术多假设外部环境是静态的,忽略了动态目标对SLAM系统的影响,这一假设很大程度上影响了无人系统自主导航的定位精度与鲁棒性。针对这一问题,提出了一种结合目标检测语义信息与多视图极线几何约束的动态SLAM系统,根据运动概率来判别与剔除系统中的动态特征点,并在TUM数据集及实机平台上进行了算法性能测试。实验结果表明在高动态环境下,该系统相较于ORB-SLAM2在绝对轨迹误差上至少可减小94%以上,相对位移轨迹和旋转角度误差分别至少降低了41%和40%,表明该算法能够有效剔除动态特征点,提高了动态环境下视觉SLAM系统的定位精度和鲁棒性。

Most existing visual SLAM systems assume that the external environment is static,ignoring the influence of dynamic objects on the SLAM system.This assumption largely affects the accuracy and robustness of autonomous navigation.To address this issue,a dynamic SLAM system was proposed,which combined semantic information based on object detection and geometric information from multi-view geometry constraints by defining and discriminating the dynamic feature points in the system based on the moving probability.Experiment results on the public TUM dataset and our robot in real environment showed that,when comparing with ORB-SLAM2,the absolute trajectory error could be reduced larger than 94%,and the average relative position and attitude errors were reduced at least 41%and 40%,respectively,in high dynamic environments.It means that the proposed SLAM system effectively removes dynamic feature points,thus improving the localization accuracy and robustness of the visual SLAM system within high dynamic environments.