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2024年6期
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THP:Tensor-field-driven hierarchical path planning for autonomous scene exploration with depth sensors
THP:Tensor-field-driven hierarchical path planning for autonomous scene exploration with depth sensors
摘要
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.
Author：
Yuefeng Xi
Chenyang Zhu
Yao Duan
Renjiao Yi
Lintao Zheng
Hongjun He
Kai Xu
作者单位：
College of Computing,National University of Defense Technology,Changsha 410073,China
年，卷(期)：
2024, 10(6)
机标分类号：
O189.13
TP242
H315.9
在线出版日期：
2025年4月23日
