An Indoor Lidar SLAM Based on Ground Constraint and Principal Component Analysis Based Feature Extraction
Aiming at the problem that the feature points extracted are not robust enough and the ac-cumulated vertical drift errors over long-term operation in current LiDAR SLAM,we propose an in-door LiDAR SLAM algorithm based on ground constraint and principal component analysis based fea-ture extraction.First,principal component analysis is applied to extract more discriminative and ro-bust features,which will improve the accuracy of feature association and pose optimization.Second,the ground is detected in the feature extraction module and the mapping module respectively and the ground constraint is added to estimate the pose.Experiments show that compared with other state-of-the-art method,the proposed algorithm can achieve better accuracy and reduce the vertical error without affecting the real-time performance in various indoor environments.
NETL
NSTL
万方数据
维普
