Accurately matching the LIDAR scans is a critical step for an Autonomous Land Vehicle (ALV). Whilst most previous works have focused on the urban environment, this paper focuses on the off-road environment. Due to the lack of a publicly available dataset for algorithm comparison, a dataset containing LIDAR pairs with varying amounts of offsets in off-road environments is firstly constructed. Several popular scan matching approaches are then evaluated using this dataset. Results indicate that global approaches, such as Correlative Scan Matching (CSM), perform best on large offset datasets, whilst local scan matching approaches perform better on small offset datasets. To combine the merits of both approaches, a two-stage fusion algorithm is designed. In the first stage, several transformation candidates are sampled from the score map of the CSM algorithm. Local scan matching approaches then start from these transformation candidates to obtain the final results. Four performance indicators are also designed to select the best transformation. Experiments on a real-world dataset prove the effectiveness of the proposed approach.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited