解决雷达点云运动失准问题，提升动态物体检测精度。
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
- 引入运动感知雷达编码器，校正移动物体导致的帧间点云错位。
- 融合雷达运动特征，使激光雷达更关注动态目标，提升检测效果。
- 在行人和骑行者检测上表现优异，适合高动态场景自动驾驶。
可靠的自动驾驶系统需要精确检测交通参与者。多模态融合已成为有效策略，特别是基于多帧4D雷达与激光雷达融合的方法，在弥补点云密度差距方面已证明有效。然而，现有方法常忽略物体运动造成的雷达点云帧间错位，且未充分利用4D雷达中的动态信息。本文提出MoRAL，一种面向鲁棒3D目标检测的运动感知多帧4D雷达与激光雷达融合框架。首先设计运动感知雷达编码器（MRE），补偿移动物体引起的帧间错位；随后提出运动注意力门控融合模块（MAGF），利用雷达运动特征引导激光雷达特征聚焦于动态前景。在View-of-Delft（VoD）数据集上的大量实验表明，MoRAL在全区域达到73.30%的最高mAP，驾驶走廊内达88.68%；对行人的AP达69.67%，骑行者在驾驶走廊内达96.25%，均优于现有方法。
原文摘要 · Abstract (English)
Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D radar and LiDAR fusion methods based on multi-frame radar point clouds have demonstrated the effectiveness in bridging the point density gap. However, they often neglect radar point clouds' inter-frame misalignment caused by object movement during accumulation and do not fully exploit the object dynamic information from 4D radar. In this paper, we propose MoRAL, a motion-aware multi-frame 4D radar and LiDAR fusion framework for robust 3D object detection. First, a Motion-aware Radar Encoder (MRE) is designed to compensate for inter-frame radar misalignment from moving objects. Later, a Motion Attention Gated Fusion (MAGF) module integrate radar motion features to guide LiDAR features to focus on dynamic foreground objects. Extensive evaluations on the View-of-Delft (VoD) dataset demonstrate that MoRAL outperforms existing methods, achieving the highest mAP of 73.30% in the entire area and 88.68% in the driving corridor. Notably, our method also achieves the best AP of 69.67% for pedestrians in the entire area and 96.25% for cyclists in the driving corridor.
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