Aiming at the problem that the localization accuracy of simultaneous localization and mapping(SLAM)algorithm is affected by the insufficient extraction of feature points in weak texture scenes,a RGB-D visual odometry algorithm integrating point-line features is proposed.The vertical dominant direction is calculated by tracking the depth information.Based on the Manhattan assumption,the line features are used to weighted search two horizontal degrees of freedom,and the Manhattan frame is extracted and optimized.The structural information of the scene is integrated with the reprojection of the point and line feature errors for joint optimization,and at the same time,adaptive weights are introduced to the residuals in the pose estimation and local map optimization to improve the pose estimation accuracy.The experimental results show that,compared with ORB-SLAM2 and MSC-VO,the absolute trajectory root mean square error of the proposed algorithm is reduced by 62.93%and 37.04%on average respectively,and is comparable to Planar-SLAM and Manhattan-SLAM in ICL-NUIM dataset;compared with ORB-SLAM2,Planar-SLAM,MSC-VO and Manhattan-SLAM,the absolute trajectory root mean square error of the proposed method is reduced by 21.43%,54.40%,35.08%and 26.94%on average in TUM dataset,respectively;and compared with ORB-SLAM2,the loop drift is reduced by 43.34%on average in TAMU dataset.
point and line featuresManhattan assumptionvisual odometry
NETL
NSTL
万方数据
