Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as autonomous driving. Despite this, there is still little research done at the intersection of optical depth sensors and radars operating close range, where the target is decimeters away from the sensors. Together with a growing interest in high-resolution imaging radars operating in the near field, the question arises how these sensors behave in comparison to their traditional optical counterparts. In this work, we take on the unique challenge of jointly characterizing depth imagers from both, the optical and radio-frequency domain using a multimodal spatial calibration. We collect data from four depth imagers, with three optical sensors of varying operation principle and an imaging radar. We provide a comprehensive evaluation of their depth measurements with respect to distinct object materials, geometries, and object-to-sensor distances. Specifically, we reveal scattering effects of partially transmissive materials and investigate the response of radio-frequency signals. All object measurements will be made public in form of a multimodal dataset, called MAROON.

翻译：利用波长特定范围或深度传感器的互补优势对于自动驾驶等鲁棒的计算机辅助任务至关重要。尽管如此，在光学深度传感器与近距离（目标距离传感器仅数分米）工作的雷达交叉领域，研究仍然较少。随着对近场高分辨率成像雷达兴趣的日益增长，这些传感器相较于传统光学对应设备的表现如何成为一个关键问题。在本工作中，我们通过多模态空间校准，对光学和射频领域的深度成像仪进行联合表征，应对这一独特挑战。我们收集了来自四个深度成像仪的数据，包括三个不同工作原理的光学传感器和一个成像雷达。我们针对不同物体材料、几何形状及物体到传感器的距离，对其深度测量进行了全面评估。具体而言，我们揭示了部分透射材料的散射效应，并研究了射频信号的响应。所有物体测量数据将以名为MAROON的多模态数据集形式公开。