Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration

TL;DR

This paper introduces a novel concave-hull based volumetric gain for robotic exploration, aligning gain calculation with the sampling-based approach to improve efficiency and robustness in environment exploration.

Contribution

It proposes a new volumetric gain method based on concave hulls derived from RRT viewpoints, addressing inconsistency issues in existing methods.

Findings

Our method reduces exploration time by approximately 62%.

It demonstrates increased robustness over state-of-the-art RRT-based exploration.

The approach effectively avoids inefficient exploration behaviors.

Abstract

The existing volumetric gain for robotic exploration is calculated in the 3D occupancy map, while the sampling-based exploration method is extended in the reachable (free) space. The inconsistency between them makes the existing calculation of volumetric gain inappropriate for a complete exploration of the environment. To address this issue, we propose a concave-hull based volumetric gain in a sampling-based exploration framework. The concave hull is constructed based on the viewpoints generated by Rapidly-exploring Random Tree (RRT) and the nodes that fail to expand. All space outside this concave hull is considered unknown. The volumetric gain is calculated based on the viewpoints configuration rather than using the occupancy map. With the new volumetric gain, robots can avoid inefficient or even erroneous exploration behavior caused by the inappropriateness of existing volumetric gain calculation methods. Our exploration method is evaluated against the existing state-of-the-art RRT-based method in a benchmark environment. In the evaluated environment, the average running time of our method is about 38.4% of the existing state-of-the-art method and our method is more robust.