M3-GMN: A Multi-Environment, Multi-LiDAR, Multi-Task Dataset for Grid Map Based Navigation

In this paper, we propose a multi-environment, multi-LiDAR, multi-task dataset to promote the grid map- based navigation capability for autonomous vehicles. The dataset comprises structured and unstructured environmental data captured by different types of LiDAR and contains various challenging scenarios, including moving objects, neg- ative obstacles, steep slopes, cliffs, overhangs, etc. Further, we have devised an innovative method for generating ground truth, facilitating the creation of dense, accurate, and stable grid maps with a minimal requirement for human annota- tion efforts. A new baseline method and two existing ap- proaches are evaluated on this dataset. Results indicate that existing approaches perform much worse than the proposed baseline. The dataset will be made publicly available at https://github.com/guanglei96/M3-GMN.