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：利用波长特异性距离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（目标距离传感器仅分米级）雷达的交叉领域，研究仍相对匮乏。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传感器的性能表现成为关键问题。在本研究中，我们通过多模态空间标定，对光学与射频领域的深度成像仪进行联合表征，应对这一独特挑战。我们采集了四台深度成像仪的数据，包括三台工作原理各异的光学传感器和一台成像雷达。我们针对不同物体材料、几何形状及物体与传感器距离，对其深度测量结果进行了全面评估。具体而言，我们揭示了部分透射性材料的散射效应，并探究了射频信号的响应特性。所有物体测量数据将以名为MAROON的多模态数据集形式公开。