外观
论文arXiv 2609.212121 个月前
带位姿注意力的视觉导航 Transformer
原文链接：Visual Navigation Transformer with Pose Attention（arXiv 2609.21212 · 发布日期：2026-09-18）
Visual Navigation Transformer with Pose Attention
为复用早期遍历经验，VNT-PA以相机位姿索引深度关键帧并用位姿差注意力，在HM3D点目标导航验证。
核心要点
- 时序历史把观测绑定到出现时间，使学习导航策略难以复用早期环境遍历经验。
- VNT-PA将深度关键帧按相机位姿组成上下文，让注意力依赖关键帧间位姿差。
- 模型模仿基于真实场景网格的最短路径规划器，仅用当前位姿和目标位置查询上下文。
- 在HM3D验证场景点目标导航达93.3%成功率和90.4% SPL，并优于相关基线。
验证范围与局限：验证范围为HM3D验证场景中的点目标导航。
英文摘要 (Abstract)
Learned navigation policies typically consume observations as a temporally ordered history, with positional encodings tying each observation to when it was seen, making it difficult to reuse experience from earlier traversals of an environment. Systems that do reuse such experience usually construct an explicit representation, such as a map or a topological graph, and plan on it. We propose VNT-PA (Visual Navigation Transformer with Pose Attention), a transformer planner whose context is a set of depth keyframes indexed by camera pose. With camera poses as positional encoding, attention depends on the pose differences between keyframes rather than on their temporal order. VNT-PA is trained to imitate a shortest-path planner operating on the ground-truth scene mesh, predicting actions by querying the spatial context with only its current pose and the goal position. On point-goal navigation in HM3D validation scenes, VNT-PA reaches 93.3% success and 90.4% success weighted by path length (SPL), outperforming baselines that encode the same context as a temporal sequence or treat pose as an input feature, in both navigation performance and training efficiency. Because the spatial context is a pose-indexed set, frames from different trajectories can be fused at test time. The planner also degrades more gracefully under localization noise than a conventional baseline which plans on explicit maps. These results show that pose-stamped experience can serve directly as the environment representation for a learned planner, and that making attention depend on pose differences, rather than on temporal order, speeds up training and improves long-horizon navigation.
作者：Beiming Li, Jaime Romero, Jonathan Diller, Vijay Kumar, Alejandro Ribeiro
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