Abstract
Accurate point-wise velocity estimation in 3D is crucial for robot interaction with non-rigid, dynamic agents, such as humans, enabling robust performance in path planning, collision avoidance, and object manipulation in dynamic environments. To this end, this paper proposes a novel RADAR, LiDAR, and camera fusion pipeline for point-wise 3D velocity estimation named CaRLi-V. This pipeline leverages raw RADAR measurements to create a novel RADAR representation, the velocity cube, which densely represents radial velocities within the RADAR's field-of-view. By combining the velocity cube for radial velocity extraction, optical flow for tangential velocity estimation, and LiDAR for point-wise range measurements through a closed-form solution, our approach can produce 3D velocity estimates for a dense array of points. Developed as an open-source ROS2 package, CaRLi-V has been field-tested against a custom dataset and proven to produce low velocity error metrics relative to ground truth, enabling point-wise velocity estimation for robotic applications.
Abstract (translated)
URL
https://arxiv.org/abs/2511.01383
