Abstract
Monocular depth estimation (MDE) provides a useful tool for robotic perception, but its predictions are often uncertain and inaccurate in challenging environments such as surgical scenes where textureless surfaces, specular reflections, and occlusions are common. To address this, we propose ProbeMDE, a cost-aware active sensing framework that combines RGB images with sparse proprioceptive measurements for MDE. Our approach utilizes an ensemble of MDE models to predict dense depth maps conditioned on both RGB images and on a sparse set of known depth measurements obtained via proprioception, where the robot has touched the environment in a known configuration. We quantify predictive uncertainty via the ensemble's variance and measure the gradient of the uncertainty with respect to candidate measurement locations. To prevent mode collapse while selecting maximally informative locations to propriocept (touch), we leverage Stein Variational Gradient Descent (SVGD) over this gradient map. We validate our method in both simulated and physical experiments on central airway obstruction surgical phantoms. Our results demonstrate that our approach outperforms baseline methods across standard depth estimation metrics, achieving higher accuracy while minimizing the number of required proprioceptive measurements.
Abstract (translated)
单目深度估计（MDE）为机器人感知提供了一个有用的工具，但在手术场景等具有挑战性的环境中，其预测结果往往存在不确定性和不准确性。这些环境通常包含无纹理表面、镜面反射和遮挡等问题。为了应对这一问题，我们提出了ProbeMDE，这是一种成本意识型的主动感知框架，结合了RGB图像与稀疏本体感受测量数据来进行单目深度估计。 我们的方法利用一组MDE模型来预测密集的深度图，这些模型不仅依赖于RGB图像，还依赖于通过本体感受获取的一组稀疏已知深度测量值。机器人在特定配置下接触环境时可以获得这些测量值。我们通过集合方差量化预测不确定性，并计算候选测量位置处不确定性的梯度。为了防止选择最具有信息量的位置来进行本体感知（触觉）时出现模式崩溃，我们在该梯度图上使用Stein变分梯度下降法（SVGD）。 我们在模拟和物理实验中对中心气道阻塞手术模型进行了方法验证。我们的结果显示，相比基线方法，在标准深度估计指标方面，我们的方法在提高准确率的同时减少了所需的本体感知测量次数。
URL
https://arxiv.org/abs/2512.11773
