基于改进ORB_SLAM2的机器人视觉导航方法

Visual Navigation Method for Robot Based on Improved ORB_SLAM2

提出了一种基于改进ORB_SLAM2的视觉导航方法。针对ORB_SLAM2构建的稀疏地图信息不充分、缺少占据信息、复用性差而无法直接用于导航的问题,对ORB_SLAM2算法进行了改进,融合环境的3D、2D占据特征以及路标点空间位置、视觉特征等多模态信息构建了包含定位、规划、交互图层的多图层地图以支撑机器人的精准定位和最优路径规划;针对机器人的自主导航任务,基于先验多图层地图建立约束进行机器人的位姿估计,融合运动约束进行机器人位姿优化,实现了基于先验地图的机器人精准定位,同时基于规划图层进行机器人的最优路径规划,提升了机器人的自主导航能力。在TUM数据集及北京鹫峰国家森林公园进行实验,结果表明:所构建的多图层地图提升了机器人定位的精度和效率,性能明显优于RGB-D SLAM;基于先验地图的视觉定位方法能够进行机器人移动过程中精准、实时地定位,助力机器人按照所规划的路径实现准确的自主导航。

Aimed at the problems of insufficient information and poor reusability of the sparse map constructed by ORB_SLAM2,a visual navigation method based on improved ORB_SLAM2 was proposed.It included two stages of building a multi-layer map and navigation.In the stage of building a multi-layer map,a local dense point cloud was calculated by the key frame of ORB_SLAM2,outliers were removed by radius filter and fast itreative closest point(FAST ICP)algorithm was used to register the processed point cloud.After that,3 D occupancy information was calculated by local dense point cloud;3 D occupancy information was extracted by means of the height of mobile robot in 3 D space and 2 D occupancy information was calculated by 2 D mapping;3 D,2 D occupancy information and 3 D,2 D features of the key frames were fused to generate a globally consistent multi-layer map.In navigation stage,according to the prior information of positioning layer,2 D features of the key frame were clustered to generate a visual dictionary,the visual dictionary was indexed according to the characteristics of current image to obtain the reference key frame;the initial pose was calculated by perspective-n-point(PnP)algorithm,and then reprojection error was used to construct inter-frame constraints,final result of localization was obtained through Gauss-Newton optimization;in planning layer,Aalgorithm was used to plan path so that mobile robot visual navigation was realized.Verified by TUM dataset,the proposed method was about 50%faster than RGB-D SLAM,and the pose estimating was almost improved by 10%,the localization result based on prior map were consistent with the original map.In addition,the experiments on the real robot platform showed that the proposed method can construct a high-precision multi-layer map,and the error between the measured value of land the real value was 6.7%,and the error between the measured value of land the real value was 5.6%,and the fast and accurate navigation was achieved on the basis of multi-layer map.