Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction

TL;DR

This paper introduces a novel exploration method that combines neural network occupancy prediction with Bayesian uncertainty to improve autonomous exploration efficiency in unknown environments.

Contribution

It presents a new approach integrating neural network-based occupancy prediction with Bayesian uncertainty for more efficient autonomous exploration.

Findings

Superior exploration efficiency demonstrated in simulations

Effective integration of neural network uncertainty into information metrics

Outperforms existing exploration strategies in realistic environments

Abstract

Autonomous exploration is a crucial aspect of robotics, enabling robots to explore unknown environments and generate maps without prior knowledge. This paper proposes a method to enhance exploration efficiency by integrating neural network-based occupancy grid map prediction with uncertainty-aware Bayesian neural network. Uncertainty from neural network-based occupancy grid map prediction is probabilistically integrated into mutual information for exploration. To demonstrate the effectiveness of the proposed method, we conducted comparative simulations within a frontier exploration framework in a realistic simulator environment against various information metrics. The proposed method showed superior performance in terms of exploration efficiency.