FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget

TL;DR

This paper introduces FIG-OP, a novel exploration method for large unknown environments that efficiently balances information gain and time constraints by frontloading exploration efforts, outperforming traditional approaches.

Contribution

The paper proposes the Frontloaded Information Gain Orienteering Problem (FIG-OP), a new approach that accounts for model uncertainty and improves exploration efficiency in large-scale environments.

Findings

FIG-OP outperforms greedy and traditional orienteering methods in coverage efficiency.

The method is effective across diverse environments like subways and mines.

Extensive field tests validate the approach's robustness and practicality.

Abstract

We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteering Problem (FIG-OP) -- a generalization of the traditional orienteering problem where the assumption of a reliable environmental model no longer holds. The FIG-OP addresses model uncertainty by frontloading expected information gain through the addition of a greedy incentive, effectively expediting the moment in which new area is uncovered. In order to reason across multi-kilometre environments, we solve FIG-OP over an information-efficient world representation, constructed through the aggregation of information from a topological and metric map. Our method was extensively tested and field-hardened across various complex environments, ranging from subway systems to mines. In comparative simulations, we observe that the FIG-OP solution exhibits improved coverage efficiency over solutions generated by greedy and traditional orienteering-based approaches (i.e. severe and minimal model uncertainty assumptions, respectively).