外观
论文arXiv 2608.213952 个月前
ODG-NoMaD：顶视相机方向引导的 NoMaD
原文链接：ODG-NoMaD: Overhead-Camera Direction-Guided NoMaD（arXiv 2608.21395 · 发布日期：2026-08-03）
ODG-NoMaD: Overhead-Camera Direction-Guided NoMaD
ODG-NoMaD用部署时顶视深度相机和机载深度为NoMaD探索提供方向，在模拟办公室环境中验证导航改进。
核心要点
- NoMaD在无目标图像和拓扑地图的未见环境中只能无方向探索，缺乏全局意识。
- 方法无需重训策略，部署时用顶视深度相机构建占据图并规划全局路径。
- 全局路径分段得到期望航向，再由机载深度的逐帧可通行图细化为避障方向。
- 通过在最终去噪步骤注入方向代价梯度，引导采样轨迹同时保留探索多模态。
验证范围与局限：仅在带和不带随机障碍的模拟办公室环境中验证。
英文摘要 (Abstract)
NoMaD [31] is a learned vision-navigation policy that unifies goal-conditioned navigation and exploration in a single goal-masked diffusion policy. In an unseen environment, however - where neither a goal image nor a topological map is available - it can only explore undirectedly, wandering without global awareness. We present ODG-NoMaD, which gives NoMaD's exploration mode a global sense of where to proceed, without retraining the policy. An overhead depth camera is used once on deployment to build an occupancy map and plan a global path, which is segmented to yield a desired heading; a per-frame traversability map from the robot's onboard depth then refines this into a collision-free direction. The gradient of a cosine direction cost is injected into the final denoising steps, rotating sampled trajectories toward this direction while preserving the multimodality of exploration. In simulated office environments with and without random obstacles, ODG-NoMaD reduces the residual distance to the target by up to an order of magnitude over unguided exploration, outperforms the point-goal cost guidance of NaviDiffusor [37], and is the only configuration that remains collision-free on every trial.
作者：Blossom Treesa Bastian, Keerthi S. Shetty, Manish Kolachalam, Rani Malhotra, Ashish Dutta
来源
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