外观
论文arXiv 2610.1610316 天前
PathLift：用于视觉导航先验的深度条件度量提升
原文链接：PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors（arXiv 2610.16103 · 发布日期：2026-10-12）
PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors
针对视觉导航策略只给方向不定距离，PathLift用传感器级深度逐点生成度量路径，并在仿真和真实轮式机器人上验证。
核心要点
- 通用视觉导航策略输出无量纲轨迹，部署时还需转换为控制器可跟踪的度量路径。
- PathLift接入RGB-D、双目或投影LiDAR深度，拟合每个路点的幅值与横向残差。
- 相比统一缩放或策略内单目深度，方法面向每个路点进行深度条件校正。
- 在多种视觉导航策略、仿真及真实轮式机器人上，闭环导航表现持续提升。
验证范围与局限：验证范围为仿真和一台真实轮式机器人，真实实验使用双目与LiDAR深度。
英文摘要 (Abstract)
Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert their unitless trajectory into a metric path that a controller can track; existing scalar lifters apply uniform scaling that misses per-waypoint corrections, while in-policy approaches that inject monocular depth into the policy inherit the prediction errors of the depth model near obstacles and at close range. We present PathLift, a per-waypoint depth-conditioned adapter that lifts a pre-trained visual-navigation policy by consuming an explicit, sensor-grade depth source (RGB-D, stereo, or projected LiDAR) and fitting per-waypoint magnitude and lateral residuals to a canonicalized prior. PathLift consistently improves closed-loop navigation across diverse visual-navigation policies in simulation and on a real wheeled robot under both stereo and LiDAR depth. By turning the policy's unitless intent into a depth-feasible metric path in a single feed-forward pass, the adapter lifts real-world success without test-time optimization.
作者：Gwanjun Shin, Hyeongseok Suh, Hongmo Jung, Jongwoo Lim
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