Abstract
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.
Abstract (translated)
单目深度估计（MDE）已被广泛应用于自动驾驶车辆和移动机器人的感知系统中。然而，现有方法在连续帧间的深度估计中往往难以保持时序一致性。这种不一致性不仅会导致抖动，还可能在深度范围突变时引发估计失败。为应对这些挑战，本文提出了一种感知一致性的单目深度估计框架，该框架利用移动机器人的轮式里程计信息，实现随时间稳定且连贯的深度预测。具体而言，我们通过连续帧间的光流估计相机位姿，并利用三角测量获得稀疏深度。这些稀疏深度估计值用于更新度量尺度的递归贝叶斯估计，进而对预训练深度估计基础模型预测的相对深度进行重缩放。所提方法在KITTI、TartanAir、MS2及自建数据集上进行了评估，展现了 robust 且精确的深度估计性能。
URL
https://arxiv.org/abs/2604.01791
