用轮速里程计提升单目深度估计的时序稳定性，减少抖动和误差。
PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistency
- 利用光流与轮速数据估算相机位姿和稀疏深度，递归更新尺度因子。
- 在KITTI、TartanAir等数据集上实现更稳定且准确的深度预测。
- 适合移动机器人或自动驾驶中需要连续深度感知的场景。
单目深度估计广泛应用于自动驾驶与移动机器人感知系统，但现有方法难以保持连续帧间的时序一致性，导致深度抖动甚至突变时失效。本文提出一种兼顾时序一致性的单目深度估计框架，利用移动机器人轮速里程计信息，实现稳定的深度预测。具体地，通过连续帧间光流三角化估计相机位姿与稀疏深度，进而更新递归贝叶斯尺度估计，再对预训练深度模型输出的相对深度进行尺度重标定。在KITTI、TartanAir、MS2及自建数据集上的实验表明，该方法显著提升了深度估计的鲁棒性与准确性。
原文摘要 · Abstract (English)
Monocular depth estimation (MDE) has been widely adopted in the perception systems of autonomous vehicles and mobile robots. However, existing approaches often struggle to maintain temporal consistency in depth estimation across consecutive frames. This inconsistency not only causes jitter but can also lead to estimation failures when the depth range changes abruptly. To address these challenges, this paper proposes a consistency-aware monocular depth estimation framework that leverages wheel odometry from a mobile robot to achieve stable and coherent depth predictions over time. Specifically, we estimate camera pose and sparse depth from triangulation using optical flow between consecutive frames. The sparse depth estimates are used to update a recursive Bayesian estimate of the metric scale, which is then applied to rescale the relative depth predicted by a pre-trained depth estimation foundation model. The proposed method is evaluated on the KITTI, TartanAir, MS2, and our own dataset, demonstrating robust and accurate depth estimation performance.
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