OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments

TL;DR

The paper introduces OTO Planner, an exploration algorithm that minimizes repeated paths and computational costs in complex environments, significantly improving efficiency and speed over existing methods.

Contribution

It presents a novel exploration planner with fast frontier updating, a viewpoint evaluation system, and viewpoint refinement to enhance exploration efficiency.

Findings

Reduces exploration time by 10-20%

Decreases movement distance by 10-20%

Speeds up frontier detection by 6-9 times

Abstract

Autonomous exploration in complex and cluttered environments is essential for various applications. However, there are many challenges due to the lack of global heuristic information. Existing exploration methods suffer from the repeated paths and considerable computational resource requirement in large-scale environments. To address the above issues, this letter proposes an efficient exploration planner that reduces repeated paths in complex environments, hence it is called "Only Travelling Once Planner". OTO Planner includes fast frontier updating, viewpoint evaluation and viewpoint refinement. A selective frontier updating mechanism is designed, saving a large amount of computational resources. In addition, a novel viewpoint evaluation system is devised to reduce the repeated paths utilizing the enclosed sub-region detection. Besides, a viewpoint refinement approach is raised to concentrate the redundant viewpoints, leading to smoother paths. We conduct extensive simulation and real-world experiments to validate the proposed method. Compared to the state-of-the-art approach, the proposed method reduces the exploration time and movement distance by 10%-20% and improves the speed of frontier detection by 6-9 times.