Forest Traversability Mapping (FTM): Traversability estimation using 3D voxel-based Normal Distributed Transform to enable forest navigation

Abstract

Autonomous navigation in dense vegetation remains an open challenge and is an area of major interest for the research community. In this paper we propose a novel traversability estimation method, the Forest Traversability Map, that gives autonomous ground vehicles the ability to navigate in harsh forests or densely vegetated environments. The method estimates travers ability in unstructured environments dominated by vegetation, void of any dominant human structures, gravel or dirt roads, with higher accuracy than the state of the art: we demonstrate an improvement of over 20% F1 score (from 0.71 to 0.91) on challenging real-world data. Our method is based on 3D voxel representation and introduces a robust colour fusion method to overcome occlusion and frequent changes of lighting conditions in these environments. We also introduce and fuse multi-return lidar measurements into our probabilistic map representation in a recursive manner. Finally, we include information of neighboring voxels to increase our ability to assess the terrain travers ability correctly. These measures improve the state-of-the-art results and allow for effective traversability estimation in very challenging, densely vegetated environments.