MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques
This work addresses the problem of sensor comparison for near-field applications like autonomous driving, but it is incremental as it focuses on dataset creation and characterization without introducing new methods.
The authors tackled the lack of research on comparing near-field high-resolution imaging radars and optical depth sensors by jointly characterizing four depth imagers, including three optical sensors and one radar, across various materials, geometries, and distances. They revealed scattering effects of partially transmissive materials and investigated radio-frequency signal responses, with all measurements made public as the MAROON dataset.
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.