提出可预测计算成本的3D探索算法，提升大环境探索效率
Asymptotically-Bounded 3D Frontier Exploration enhanced with Bayesian Information Gain
- 基于八叉树地图，通过前后向传感器建模实现前沿检测与维护
- 引入贝叶斯回归估算信息增益，避免逐个统计未知体素
- 在不同尺度下探索时间最多快54%，适合大规模机器人探索
大型环境中的机器人探索因处理海量前沿而计算开销巨大。本文提出一种基于OctoMap的前沿探索算法，具备可预测且渐近有界的性能。与复杂度随环境规模增长的传统方法不同，本方法将复杂度控制在$\mathcal{O}(|\mathcal{F}|)$，其中$|\mathcal{F}|$为前沿数量。通过策略性前向与逆向传感器建模，实现近似但高效的前沿检测与维护。为进一步提升性能，引入贝叶斯回归器估算信息增益，避免显式计数未知体素以优先选择视角。仿真表明，该方法比现有OctoMap基线更高效，在不同空间尺度下总探索时间最多提升54%，且保证任务完成。真实世界实验验证了计算边界及增强效果。
原文摘要 · Abstract (English)
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.
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