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. Project page: https://brittonjordan.github.io/probe_mde/

翻译：单目深度估计（MDE）为机器人感知提供了有用的工具，但其预测在诸如手术场景等具有挑战性的环境中常常存在不确定性和不准确性，这类环境中无纹理表面、镜面反射和遮挡现象普遍存在。为解决此问题，我们提出了ProbeMDE，一个成本感知的主动感知框架，它将RGB图像与稀疏的本体感知测量相结合用于MDE。我们的方法利用一个MDE模型集成，基于RGB图像以及通过本体感知获得的一组稀疏已知深度测量值来预测稠密深度图，其中机器人以已知构型接触了环境。我们通过模型集成的方差来量化预测不确定性，并测量不确定性相对于候选测量位置的梯度。为防止在选择最具信息量的本体感知（触摸）位置时出现模式崩溃，我们在此梯度图上利用Stein变分梯度下降（SVGD）方法。我们在中央气道阻塞手术体模的仿真和物理实验中验证了我们的方法。结果表明，我们的方法在标准深度估计指标上优于基线方法，在最小化所需本体感知测量次数的同时实现了更高的准确性。项目页面：https://brittonjordan.github.io/probe_mde/