HPHS: Hierarchical Planning based on Hybrid Frontier Sampling for Unknown Environments Exploration

TL;DR

This paper introduces HPHS, a hierarchical planning method using hybrid frontier sampling from LiDAR data to efficiently explore unknown environments, reducing planning complexity and improving exploration speed.

Contribution

The paper presents a novel hierarchical planning framework combined with hybrid frontier sampling directly from LiDAR data for faster autonomous exploration.

Findings

Effective in reducing exploration time

Improves coverage of unknown environments

Demonstrates superior performance in simulations and real-world tests

Abstract

Rapid sampling from the environment to acquire available frontier points and timely incorporating them into subsequent planning to reduce fragmented regions are critical to improve the efficiency of autonomous exploration. We propose HPHS, a fast and effective method for the autonomous exploration of unknown environments. In this work, we efficiently sample frontier points directly from the LiDAR data and the local map around the robot, while exploiting a hierarchical planning strategy to provide the robot with a global perspective. The hierarchical planning framework divides the updated environment into multiple subregions and arranges the order of access to them by considering the overall revenue of the global path. The combination of the hybrid frontier sampling method and hierarchical planning strategy reduces the complexity of the planning problem and mitigates the issue of region remnants during the exploration process. Detailed simulation and real-world experiments demonstrate the effectiveness and efficiency of our approach in various aspects. The source code will be released to benefit the further research.