3-D Grid-Based VDBSCAN Clustering and Radar-Monocular Depth Completion

Accurate depth estimation is crucial in enabling unmanned aerial vehicles (UAVs) to 3-D perception, mapping, and navigation, deciding the successful completion of the flight mission. To achieve accurate perception of dense depth, we propose a novel radar-monocular associated depth completion method. First, we introduce the 3-D grid-based variable DBSCAN (3-D grid-based VDBSCAN) clustering method, which incorporates an adaptive neighbor search radius, variable density threshold, and elliptical cylindrical search region. This approach effectively addresses the problem of high variation in 4-D radar data density and the focus of measurements on the surface of the object. Second, we use Midas to estimate the inverse depth from the monocular and propose a region-search-based nearest neighbor matching approach for the fusion of radar and camera. Finally, we establish an experiment platform and conduct a thorough qualitative and quantitative evaluation. The experimental results demonstrate the robustness of the proposed 3-D grid-based VDBSCAN method in both static and dynamic scenes, with a mean recall of 0.937 and a mean V-Measure of 0.943. Moreover, the proposed data fusion method effectively recovers the depth of the scene, yielding a mean absolute relative (Abs Rel) error of 0.134 in our experiment platform.