融合相机、雷达与激光雷达，实现高精度3D点级速度估计。
CaRLi-V: Camera-RADAR-LiDAR Point-Wise 3D Velocity Estimation
- 构建雷达速度立方体，结合光流与激光雷达数据解算3D速度。
- 在自建数据集上速度误差低，优于现有场景流方法。
- 开源ROS2工具包，适合自动驾驶与机器人动态避障场景。
精确的点级3D速度估计对机器人与非刚性动态物体交互至关重要，可提升路径规划、碰撞规避和物体操作在动态环境中的鲁棒性。本文提出一种名为CaRLi-V的新颖雷达（RADAR）、激光雷达（LiDAR）与相机融合管道，用于点级3D速度估计。该管道利用原始雷达测量构建新型雷达表示——速度立方体，密集编码雷达径向速度。通过结合速度立方体提取径向速度、光流估计切向速度，并利用激光雷达点云进行点级距离测量，采用闭式解法实现稠密点阵的3D速度估计。CaRLi-V作为开源ROS2包已部署于自建数据集，相较于真实值表现出低速度误差，性能超越当前先进场景流方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。