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, negative 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 annotation efforts. A new baseline method and two existing approaches 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.