动态场景下基于语义和光流约束的视觉同步定位与地图构建

针对动态场景下的定位与静态语义地图构建问题，提出了一种基于语义和光流约束的动态环境下的同步定位与地图构建（SLAM）算法，以降低动态物体对定位与建图的影响。首先，对于输入的每一帧，通过语义分割获得图像中物体的掩模，再通过几何方法过滤不符合极线约束的特征点；接着，结合物体掩模与光流计算出每个物体的动态概率，根据动态概率过滤特征点以得到静态特征点，再利用静态特征点进行后续的相机位姿估计；然后，基于RGB-D图片和物体动态概率建立静态点云，并结合语义分割建立语义八叉树地图。最后，基于静态点云与语义分割创建稀疏语义地图。公共TUM数据集上的测试结果表明，高动态场景下，所提算法与ORB-SLAM2相比，在绝对轨迹误差和相对位姿误差上能取得95%以上的性能提升，与DS-SLAM、DynaSLAM相比分别减小了41%和11%的绝对轨迹误差，验证了该算法在高动态场景中具有较好的定位精度和鲁棒性。地图构建的实验结果表明，所提算法创建了静态语义地图，与点云地图相比，稀疏语义地图的存储空间需求量降低了99%。

Visual simultaneous localization and mapping based on semantic and optical flow constraints in dynamic scenes

For the localization and static semantic mapping problems in dynamic scenes， a Simultaneous Localization And Mapping （SLAM） algorithm in dynamic scenes based on semantic and optical flow constraints was proposed to reduce the impact of moving objects on localization and mapping. Firstly， for each frame of the input， the masks of the objects in the frame were obtained by semantic segmentation， then the feature points that do not meet the epipolar constraint were filtered out by the geometric method. Secondly， the dynamic probability of each object was calculated by combining the object masks with the optical flow， the feature points were filtered by the dynamic probabilities to obtain the static feature points， and the static feature points were used for the subsequent camera pose estimation. Then， the static point cloud was created based on RGB-D images and object dynamic probabilities， and the semantic octree map was built by combining the semantic segmentation. Finally， the sparse semantic map was created based on the static point cloud and the semantic segmentation. Test results on the public TUM dataset show that， in highly dynamic scenes， the proposed algorithm improves the performance on both the absolute trajectory error and relative pose error by more than 95% compared with ORB-SLAM2， and reduces the absolute trajectory error by 41% and 11% compared with DS-SLAM and DynaSLAM respectively， which verifies that the proposed algorithm has better localization accuracy and robustness in highly dynamic scenes. The experimental results of mapping show that the proposed algorithm creates a static semantic map， and the storage space requirement of the sparse semantic map is reduced by 99% compared to that of the point cloud map.