FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments

TL;DR

This paper introduces FSMP, a novel exploration planner combining frontier and sampling strategies, enabling fast and efficient autonomous exploration of large 3-D environments with MAVs, validated through simulations and real-world tests.

Contribution

The paper presents a new integrated frontier-sampling planner with a deterministic sampling approach and a two-stage path planning method for rapid 3-D environment exploration.

Findings

Outperforms existing methods in exploration speed and efficiency

Reduces computational time significantly

Achieves larger explored volumes in less time

Abstract

In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed for further improving the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume.