基于语义信息和边缘一致性的鲁棒SLAM算法

Robust SLAM Algorithm Based on Semantic Information and Edge Consistency

为解决动态环境中视觉定位精度下降、鲁棒性不足的问题,并改善构建的环境地图,提出一种基于语义信息和边缘一致性的鲁棒同时定位与地图创建(SLAM)算法.首先使用YOLOv3算法获取环境语义信息,得到初步的图像语义动静态分割.而后使用基于图像中边缘的距离变换误差和光度误差的一致性评估,进一步对图像的动静态区域进行细分,并利用连通区域分析和漏洞修补算法修正动态区域.使用图像非动态区域的特征点进行特征匹配,利用非线性优化算法最小化特征点的重投影误差,得到优化的相机位姿.利用特征点共视性和动静态区域面积进行绘图关键帧的选取,从而构建不包含动态物体信息的静态环境地图.公开数据集中高动态环境的实验表明,本文算法能够准确地区分图像中的动静态信息,完成动态环境下的精确定位与地图构建任务.并且本文算法在纯静态环境下不存在定位精度下降的情况.

To handle the performance degradation and the insufficient robustness of visual localization in dynamic environments,and to improve the created environment map,a robust simultaneous localization and mapping(SLAM)algorithm based on the semantic information and the edge consistency is proposed.Firstly,the semantic information of the environment is acquired by YOLOv3 algorithm,and the semantically dynamic-static segmentations of the image are obtained preliminarily.The consistency evaluation is conducted based on the distance transform errors and photometric errors of edges in the images to refine the dynamic-static area.Moreover,the dynamic regions are corrected by the connected component analysis and the loophole mending algorithm.The feature points in the non-dynamic regions are matched and the camera poses are optimized by minimizing the reprojection errors of feature points by the nonlinear optimization algorithm.The mapping keyframes are selected based on the covisibility of feature points and the areas of the dynamic-static regions.And the static environment map is created without the information of the dynamic objects.The experiment on the highly dynamic scenes in the public datasets show that the proposed method can distinguish the dynamic-static information accurately,and perform the precise localization and mapping in dynamic environments.Besides,the degradation of the positioning accuracy does not exist in the proposed method in the static environment.