Accurate point-wise velocity estimation in 3D is crucial for robot interaction with non-rigid dynamic agents, 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 encodes RADAR radial velocities. 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 on a custom dataset and achieves low velocity error metrics relative to ground truth while outperforming state-of-the-art scene flow methods.

翻译：精确的逐点三维速度估计对于机器人与非刚性动态智能体交互至关重要，可确保机器人在动态环境中的路径规划、碰撞规避和物体操控中实现稳健性能。为此，本文提出一种新型雷达、激光雷达和相机融合管线——CaRLi-V，用于逐点三维速度估计。该管线利用原始雷达测量值构建一种新颖的雷达表征——速度立方体，可密集编码雷达径向速度。通过结合径向速度提取的速度立方体、切向速度估计的光流以及利用闭式解获取逐点距离测量的激光雷达，我们的方法能够为密集点阵列生成三维速度估计。作为开源的ROS2软件包开发，CaRLi-V已在自定义数据集上完成实地测试，相对于真实基准实现了较低的速度误差指标，并超越了最先进的场景流方法。