@article{Xi2024, 
author = {Yuefeng Xi and Chenyang Zhu and Yao Duan and Renjiao Yi and Lintao Zheng and Hongjun He and Kai Xu},
title = {THP: Tensor-field-driven hierarchical path planning for autonomous scene exploration with depth sensors},
year = {2024},
journal = {Computational Visual Media},
volume = {10},
number = {6},
pages = {1121-1135},
keywords = {path planning, trajectory optimization, tensor field, indoor scene exploration},
url = {https://www.sciopen.com/article/10.1007/s41095-022-0312-6},
doi = {10.1007/s41095-022-0312-6},
abstract = {It is challenging to automatically explore an unknown 3D environment with a robot only equipped with depth sensors due to the limited field of view. We introduce THP, a tensor field-based framework for efficient environment exploration which can better utilize the encoded depth information through the geometric characteristics of tensor fields. Specifically, a corresponding tensor field is constructed incrementally and guides the robot to formulate optimal global exploration paths and a collision-free local movement strategy. Degenerate points generated during the exploration are adopted as anchors to formulate a hierarchical TSP for global path optimization. This novel strategy can help the robot avoid long-distance round trips more effectively while maintaining scanning completeness. Furthermore, the tensor field also enables a local movement strategy to avoid collision based on particle advection. As a result, the framework can eliminate massive, time-consuming recalculations of local movement paths. We have experimentally evaluate our method with a ground robot in 8 complex indoor scenes. Our method can on average achieve 14% better exploration efficiency and 21% better exploration completeness than state-of-the-art alternatives using LiDAR scans. Moreover, compared to similar methods, our method makes path decisions 39% faster due to our hierarchical exploration strategy.}
}