Feature-based 3D outdoor slam with local filters

In this paper, a novel method of extracting 3D planar features from laser range data (LiDAR) and its adaptation to outdoor SLAM using respective extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. Firstly, the feature extraction from 3D LiDAR data using a probabilistic plane extraction method and the merging procedure is explained. Then the extracted 3D planar features are adapted to the well-known local filters to solve the simultaneous localization and mapping (SLAM) problem. Finally, the method is evaluated with the real datasets and the results show that EKF and UKF have very similar performance, and they can be used in plane-feature-based SLAM problems successfully.