让水下视觉机器人更懂自己感知的不确定性
Uncertainty Aware Mapping for Vision-Based Underwater Robots
- 用RAFT-Stereo估计深度与置信度，融合进体素地图框架
- 改进Voxblox权重计算和更新机制，降低误判风险
- 在池塘和特罗姆瑟峡湾码头验证，可视化不确定性变化
基于视觉的水下机器人可在传统传感器无法覆盖的狭小空间中执行检查与探索任务。然而，传感器噪声和环境变化会导致环境表征出现显著不确定性。本文研究如何在视觉感知中表示映射不一致性，并将深度估计的置信度融入映射框架。通过RAFS-Stereo模型估计场景深度与置信度，并将其集成至体素地图框架Voxblox中。同时，对现有Voxblox的权重计算与更新机制进行改进。最后，在封闭水池及特罗姆瑟峡湾码头的水下机器人实验中进行了定性分析，验证了所提方法在不确定性可视化方面的有效性。
原文摘要 · Abstract (English)
Vision-based underwater robots can be useful in inspecting and exploring confined spaces where traditional sensors and preplanned paths cannot be followed. Sensor noise and situational change can cause significant uncertainty in environmental representation. Thus, this paper explores how to represent mapping inconsistency in vision-based sensing and incorporate depth estimation confidence into the mapping framework. The scene depth and the confidence are estimated using the RAFT-Stereo model and are integrated into a voxel-based mapping framework, Voxblox. Improvements in the existing Voxblox weight calculation and update mechanism are also proposed. Finally, a qualitative analysis of the proposed method is performed in a confined pool and in a pier in the Trondheim fjord. Experiments using an underwater robot demonstrated the change in uncertainty in the visualization.
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