用改进的扩散模型实时预测未知环境占位，提升地图前沿推理能力。
Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation
- 移除视觉注意力模块，提速73%且精度损失小
- 实现全地图占位预测，突破相机视野限制
- 概率融合方法使前沿占位预测准确率提升71%
在未测绘环境中实现自主导航与探索仍是机器人领域的重大挑战，主要源于机器人难以对未观测几何结构进行常识性推断。近期研究显示，生成建模技术（尤其是扩散模型）可从局部观测中推断出缺失几何结构。本文提出一种改进的扩散模型，用于实时在线占位预测。通过移除基于注意力的视觉条件和视觉特征提取组件，运行时间减少73%，同时保持几乎不变的精度。该修改使占位预测覆盖整个地图，而非局限于摄像头可采集数据的区域。我们引入一种概率更新方法，将预测的占位数据融合进动态占位地图，相较以往方法，地图前沿占位预测性能提升71%。最后，我们将在发表后公开代码及ROS节点，地址为github.com/arpg/sceneSense_ws。
原文摘要 · Abstract (English)
Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generative modeling techniques, particularly diffusion models, can enable systems to infer these geometries from partial observation. In this work, we present implementation details and results for real-time, online occupancy prediction using a modified diffusion model. By removing attention-based visual conditioning and visual feature extraction components, we achieve a 73$\%$ reduction in runtime with minimal accuracy reduction. These modifications enable occupancy prediction across the entire map, rather than being limited to the area around the robot where camera data can be collected. We introduce a probabilistic update method for merging predicted occupancy data into running occupancy maps, resulting in a 71$\%$ improvement in predicting occupancy at map frontiers compared to previous methods. Finally, we release our code and a ROS node for on-robot operation <upon publication> at github.com/arpg/sceneSense_ws.
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