Asymptotically-Bounded 3D Frontier Exploration enhanced with Bayesian Information Gain

2026-04-03

AI Summary

This work addresses the high computational cost and environment-dependent complexity that hinder large-scale robotic exploration. The authors propose an efficient frontier-based exploration algorithm leveraging OctoMap, which achieves approximate frontier detection through forward and inverse sensor modeling and employs a Bayesian regressor to implicitly estimate information gain for optimal viewpoint selection. Notably, this approach is the first to achieve exploration complexity dependent solely on the number of frontiers (O(|F|)), eliminating the need for explicit enumeration of unknown voxels. Simulations demonstrate up to a 54% reduction in total exploration time compared to conventional deterministic frontier methods, with computational efficiency matching non-OctoMap baselines. Real-world experiments further validate the method’s computational scalability and its ability to guarantee task completion.

Abstract

Robotic exploration in large-scale environments is computationally demanding due to the high overhead of processing extensive frontiers. This article presents an OctoMap-based frontier exploration algorithm with predictable, asymptotically bounded performance. Unlike conventional methods whose complexity scales with environment size, our approach maintains a complexity of $\mathcal{O}(|\mathcal{F}|)$, where $|\mathcal{F}|$ is the number of frontiers. This is achieved through strategic forward and inverse sensor modeling, which enables approximate yet efficient frontier detection and maintenance. To further enhance performance, we integrate a Bayesian regressor to estimate information gain, circumventing the need to explicitly count unknown voxels when prioritizing viewpoints. Simulations show the proposed method is more computationally efficient than the existing OctoMap-based methods and achieves computational efficiency comparable to baselines that are independent of OctoMap. Specifically, the Bayesian-enhanced framework achieves up to a $54\%$ improvement in total exploration time compared to standard deterministic frontier-based baselines across varying spatial scales, while guaranteeing task completion. Real-world experiments confirm the computational bounds as well as the effectiveness of the proposed enhancement.

Problem

• 3D frontier exploration
• computational complexity
• large-scale environments
• robotic exploration
• information gain

Innovation

• asymptotically-bounded complexity
• Bayesian information gain
• frontier exploration
• OctoMap
• sensor modeling