为提高 ORB-SLAM2(oriented fast and rotated brief,and simultaneous localization and mapping)系统的位姿估计精度并解决仅能生成稀疏地图的问题,提出了融合迭代最近点拟合(iterative closest point,ICP)算法与曼哈顿世界假说的位姿估计策略并在系统中加入稠密建图线程.首先通过ORB(oriented fast and rotated brief)特征点法、最小显著性差异(least-significant difference,LSD)算法和聚集层次聚类(agglomerative hierarchical clustering,AHC)方法提取点、线、面特征,其中点、线特征与上一帧匹配,面特征在全局地图匹配.然后采用基于surfel的稠密建图策略将图像划分为非平面与平面区域,非平面采用ICP算法计算位姿,平面则通过面与面的正交关系确定曼哈顿世界从而使用不同估计策略,其中曼哈顿世界场景通过位姿解耦实现基于曼哈顿帧观测的无漂移旋转估计,而该场景的平移以及非曼哈顿世界场景的位姿采用追踪的点、线、面特征进行估计和优化;最后根据关键帧和相应位姿实现稠密建图.采用慕尼黑工业大学(technische universität munchen,TUM)数据集验证所提建图方法,经过与ORB-SLAM2算法比较,均方根误差平均减少0.24 cm,平均定位精度提高7.17％,验证了所提方法进行稠密建图的可行性和有效性.
Improved Pose Estimation for Dense Mapping Based on ORB-SLAM
In order to improve the position estimation accuracy of ORB-SLAM2 system and solve the problem that only generate sparse maps,proposing position estimation strategy integrating the ICP(iterative closest point)algorithm and the Manhattan world hy-pothesis and adding dense-map building thread on the system.Firstly,extracting point,line and surface features by ORB(oriented fast and rotated brief)feature point method,LSD(least-significantdifference)algorithm and AHC(agglomerative hierarchical clustering)method,in which matching the point and line features with previous frame and matching the surface features in the global map.Then the image was divided into non-planar and planar raegions using surfel-based dense-map building strategy,the non-plane uses the ICP algorithm to compute pose,and the plane determines Manhattan world through the orthogonal relationship between plane and plane to use different estimation strategies.The Manhattan world scene realizes the drift-free rotation estimation based on the observation of the Manhattan frame through position decoupling,the translation of the scene and the pose of the non-Manhattan world scene were esti-mated and optimized by the traced points,lines and surface features.Finally,according to the key frame and the corresponding pose to realize the dense-map.The TUM(technische universität munchen)dataset was used to verify the map method,comparing with ORB-SLAM2 algorithm,RMSE(root mean squared error)decreased by 0.24 cm and the average positioning accuracy increased by 7.17％,which verify the feasibility and effectiveness of the proposed method for the dense map.
position estimateplaneManhattan world hypothesissimultaneous localization and mapping(SLAM)dense mapping
万方数据
维普
