MVD-VE: Motion Variation–Based Dynamic Detection and Velocity Estimation with 3D LiDAR

With the rapid development of environmental perception technologies and intelligent transportation, accurately detecting and estimating the motion states of dynamic objects has become increasingly important. This study proposes the motion variation–based dynamic detection and velocity estimation (MVD-VE) system, based on 3D LiDAR point cloud data, aiming for high-precision detection and velocity estimation of dynamic objects. The system first utilizes the Multiple Signal Classification (MUSIC) based Ground Normal Estimation (MUSIG) for dynamic ground segmentation, effectively eliminating interference and enhancing detection efficiency. Subsequently, a hierarchical detection framework is applied, integrating range image variations and bird’s-eye view (BEV) features, such as Vertical Robust Height Range (VRHR) and Cell Occupancy Density (COD), to identify potential dynamic points during coarse detection. A localized-to-global optimization strategy is then used to further refine the detection results, improving accuracy and robustness. For velocity estimation, we propose a Robust Iterative Closest Point (R-ICP) algorithm enhanced with adaptive point-pair filtering and termination criteria, addressing outliers and convergence instability. Experiments with a VLP-16 LiDAR show 93.3% peak F1-score in dynamic detection and 0.35 m/s MAE in velocity estimation, validating the system’s efficacy for autonomous motion planning and safety-critical applications.