This paper investigates the real time LiDAR odometry and mapping (LOAM) problem in unstructured environments. We propose E-LOAM (LOAM with Expanded Local Structural Information), a paradigm which expands the pre-extracted geometric information with local point cloud information around the geometric feature points. State-of-the-art approaches usually extract pointed geometric features as the only correspondence primitives for point cloud scan-to-scan and scan-to-map registration. We argue that, in unstructured environments, sometimes, the extracted geometric features are too sparse for adequate point cloud registration. Therefore, E-LOAM expands the ‘pointed’ geometric correspondence primitives with the point clouds around them, i.e., the local point clouds in the voxel around the feature point. The local point clouds, approximated by a multivariate normal distribution, offer additional local structural information, on top of the pointed geometric information. Additionally, to enrich the sparse geometric features, we make use of the intensity information of point clouds, and extract the places with high intensity variations as additional feature points. Experimental results with KITTI dataset show the efficacy of E-LOAM, when compared with state of the arts. We further implement E-LOAM on a real robot platform, and evaluate E-LOAM with in-field tests.
This paper investigates the real time LiDAR odometry and mapping (LOAM) problem in unstructured environments. We propose E-LOAM (LOAM with Expanded Local Structural Information), a paradigm which expands the pre-extracted geometric information with local point cloud information around the geometric feature points. State-of-the-art approaches usually extract pointed geometric features as the only correspondence primitives for point cloud scan-to-scan and scan-to-map registration. We argue that, in unstructured environments, sometimes, the extracted geometric features are too sparse for adequate point cloud registration. Therefore, E-LOAM expands the ‘pointed’ geometric correspondence primitives with the point clouds around them, i.e., the local point clouds in the voxel around the feature point. The local point clouds, approximated by a multivariate normal distribution, offer additional local structural information, on top of the pointed geometric information. Additionally, to enrich the sparse geometric features, we make use of the intensity information of point clouds, and extract the places with high intensity variations as additional feature points. Experimental results with KITTI dataset show the efficacy of E-LOAM, when compared with state of the arts. We further implement E-LOAM on a real robot platform, and evaluate E-LOAM with in-field tests.