诊断与动态过滤占用世界模型以实现主动建图
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
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中文总结 AI 辅助
本研究通过固定规划器并改变占用表示，诊断占用误差对主动建图的影响，发现单独纠正假阳性或假阴性无法持续改善覆盖率，并提出一种动态过滤策略以提升覆盖效率。
中文摘要 AI 辅助
主动建图要求机器人选择能够高效重建未知三维场景的相机视角。为了推理未观测区域，近期系统使用预训练的占用网络作为世界模型，以补全缺失的几何结构。预测的结构有助于期望覆盖增益的计算，并约束可行的机器人运动。因此，占用误差可能同时改变机器人选择探索的内容及其能够移动的位置。我们通过保持规划器固定、仅改变提供给它的占用表示来诊断这些影响。我们考虑了无补全规划、使用学习到的占用规划、通过地面真值预言机去除假阳性、通过预言机恢复假阴性以及使用地面真值占用规划等情形。我们的实验表明，单独纠正假阳性或假阴性并不能持续改善最终覆盖率。这一发现揭示了占用精度与下游规划性能之间的差距。地面真值占用在覆盖效率上的提升远大于在端点覆盖率上的提升，这表明即使几何世界模型准确，规划和可达性仍然是重要的瓶颈。基于这些发现，我们提出了一种动态过滤策略，该策略在未探索空间中保留预测，同时利用在线观测抑制反复不受支持的占用。初步示例表明，该策略可以将视角选择引导至可达表面，否则这些表面将保持未观测状态。
英文摘要
Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, recent systems use pretrained occupancy networks as world models that complete missing geometry. The predicted structure contributes to expected coverage gain and constrains feasible robot motion. Consequently, occupancy errors can change both what the robot chooses to explore and where it is able to move. We diagnose these effects by holding the planner fixed and varying only the occupancy representation provided to it. We consider planning without completion, with learned occupancy, with false positives removed by a ground truth oracle, with false negatives restored by an oracle, and with ground truth occupancy. Our experiments show that correcting false positives or false negatives alone does not consistently improve final coverage. This finding reveals a gap between occupancy accuracy and downstream planning performance. Ground truth occupancy provides a much larger improvement in coverage efficiency than in endpoint coverage, suggesting that planning and reachability remain important bottlenecks even when the geometric world model is accurate. Based on these findings, we introduce a dynamic filtering strategy that preserves predictions in unexplored space while suppressing repeatedly unsupported occupancy using online observations. Preliminary examples show that this strategy can redirect viewpoint selection toward reachable surfaces that would otherwise remain unobserved.
发表机构
- Boise State University（博伊西州立大学）
- Texas A&M University–San Antonio（德克萨斯农工大学圣安东尼奥分校）
机构由 AI 辅助整理，请以论文原文为准。
