FineNav: A Versatile Framework Enhancing Ground Robot Navigation in Unstructured Environment
Jinghui Wang, Chenyang Wang, Yuxuan Cao, Zelong Sun, Wang Xi, Jianping He
Abstract
Autonomous navigation of ground robots in unstructured 3D environments remains a fundamental challenge, as it requires accommodating dynamic obstacles, non-planar ground, and multi-story structures within a unified framework. In this paper, we propose a versatile navigation framework named FineNav. It features a novel hierarchical mapping system that couples a high-rate local voxel grid for real-time perception with a scalable global octree for persistent storage. This design balances low-latency performance with large-scale mapping capabilities, enabling reliable navigation in unstructured environments. Moreover, the entire navigation pipeline is refactored into modular and reusable components, while maintaining compatibility with existing 2D navigation ecosystems. We validate FineNav on a wheeled robot, demonstrating its versatility across diverse scenarios. FineNav is released as open-source software for the community.