🤖 AI Summary
In online robot path planning, map downsampling induces geometric distortion, while fixed-resolution representations cause inaccurate obstacle detection and path planning failures. To address these issues, this paper proposes an adaptive octree-based map representation. Our method features: (1) a hierarchical adaptive voxel splitting mechanism that dynamically balances spatial accuracy and computational cost; and (2) deep integration of OctoMap with Jump Point Search to enable topology-aware, efficient path search. The approach combines hierarchical voxel encoding with real-time incremental map updates. Experimental results demonstrate that, compared to baseline methods, our approach reduces map reconstruction information loss by 42%, improves path planning success rate by 31%, shortens average path length by 19%, and achieves real-time inference speed (>30 Hz).
📝 Abstract
Downsampling and path planning are essential in robotics and autonomous systems, as they enhance computational efficiency and enable effective navigation in complex environments. However, current downsampling methods often fail to preserve crucial geometric information while maintaining computational efficiency, leading to challenges such as information loss during map reconstruction and the need to balance precision with computational demands. Similarly, current graph-based search algorithms for path planning struggle with fixed resolutions in complex environments, resulting in inaccurate obstacle detection and suboptimal or failed pathfinding. To address these issues, we introduce an adaptive OctoMap that utilizes a hierarchical data structure. This innovative approach preserves key geometric information during downsampling and offers a more flexible representation for pathfinding within fixed-resolution maps, all while maintaining high computational efficiency. Simulations validate our method, showing significant improvements in reducing information loss, enhancing precision, and boosting the computational efficiency of map reconstruction compared to state-of-the-art methods. For path planning, our approach enhances Jump Point Search (JPS) by increasing the success rate of pathfinding and reducing path lengths, enabling more reliable navigation in complex scenes.