PathLift：用于视觉导航先验的深度条件度量提升
PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors
AI总结：
PathLift是一种深度条件适配器，可提升预训练视觉导航策略，在仿真与真实机器人上均能改善闭环导航性能，无需测试时优化即可提升现实世界导航成功率。
摘要：
通用视觉导航策略会告知机器人前进方向，但不会告知距离，部署时需要单独的提升步骤将其无量纲轨迹转换为控制器可跟踪的度量路径；现有标量提升器采用均匀缩放，无法实现每个路标点的修正，而将单目深度注入策略的策略内方法会继承深度模型在障碍物附近及近距离的预测误差。我们提出PathLift，一种每个路标点的深度条件适配器，通过使用显式传感器级深度源（RGB-D、立体视觉或投影激光雷达）并为归一化先验拟合每个路标点的幅值和横向残差，来提升预训练的视觉导航策略。PathLift在仿真环境及真实轮式机器人上，采用立体视觉和激光雷达深度时，均能持续提升各类视觉导航策略的闭环导航性能。该适配器通过单次前向传播将策略的无量纲意图转换为深度可行的度量路径，无需测试时优化即可提升现实世界的任务成功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
