基于几何⁃语义联合约束的动态环境视觉SLAM算法

Dynamic Visual SLAM Based on Unified Geometric⁃Semantic Constraints

传统视觉同步定位和地图构建(Simultaneous localization and mapping,SLAM)算法建立在静态环境假设的基础之上,当场景中出现动态物体时,会影响系统稳定性,造成位姿估计精度下降。现有方法大多基于概率统计和几何约束来减轻少量动态物体对视觉SLAM系统的影响,但是当场景中动态物体较多时,这些方法失效。针对这一问题,本文提出了一种将动态视觉SLAM算法与多目标跟踪算法相结合的方法。首先采用实例语义分割网络,结合几何约束,在有效地分离静态特征点和动态特征点的同时,进一步实现多目标跟踪,改善跟踪结果,并能够获得运动物体的轨迹和速度矢量信息,从而能够更好地为机器人自主导航提供决策信息。在KITTI数据集上的实验表明,该算法在动态场景中相较ORB⁃SLAM2算法精度提高了28%。

Traditional visual simultaneous localization and mapping(SLAM)algorithms rely on the scene rigidity assumption.However,when dynamic objects exist in the scene,the stability of the SLAM system will be affected and the accuracy of pose estimation will be reduced.Currently,most of the existing methods apply probability strategies and geometric constraints to reduce the impact caused by a small number of dynamic objects.But when the number of dynamic objects in the scene is high,these methods will fail.In order to deal with this problem,a novel algorithm is proposed in this paper.It combines the dynamic visual SLAM algorithm with the multi⁃target tracking algorithm.Firstly,a semantic instance segmentation network together with geometric constraints is introduced to assist the visual SLAM module to effectively separate the static feature points from the dynamic ones,and at the same time,it can also achieve the better multi⁃target tracking performance.Furthermore,the trajectory and velocity information of the moving objects can also be estimated,which can provide decision information for autonomous robots navigation.The experimental results on KITTI dataset show that the localization accuracy of the proposed algorithm is improved by about 28%compared with ORB⁃SLAM2 algorithm in dynamic environments.