用4D雷达实现恶劣环境下的精准定位与地图构建
Super4DR: 4D Radar-centric Self-supervised Odometry and Gaussian-based Map Optimization
- 通过聚类感知网络和分层自监督机制提升雷达帧间匹配精度
- 采用3D高斯表示，使地图结构更清晰完整，性能提升67%
- 适合自动驾驶、机器人在雨雾等恶劣条件下的导航应用
基于视觉或激光雷达的传统里程计与地图构建方法在光照不足和恶劣天气下表现不佳。尽管4D雷达适用于此类环境，但其点云稀疏且噪声大，导致里程计估计不准确，地图结构模糊不完整。为此，我们提出Super4DR，一种以4D雷达为中心的自监督里程计与高斯地图优化框架。首先，设计了融合聚类雷达点对象级线索的里程计网络，结合时空一致性、知识迁移与特征对比的分层自监督机制，有效缓解异常值影响；其次，采用3D高斯作为中间表示，配合雷达专用生长策略、选择性分离与多视角正则化，恢复模糊区域及图像纹理未覆盖区域。实验表明，Super4DR相比先前自监督方法性能提升67%，接近有监督里程计水平，并显著缩小与激光雷达的地图质量差距，支持多模态图像渲染。
原文摘要 · Abstract (English)
Conventional odometry and mapping methods using visual or LiDAR data often struggle under poor illumination and adverse weather conditions. Although 4D radar is suited for such environments, its sparse and noisy point clouds hinder accurate odometry estimation, while the radar maps suffer from obscure and incomplete structures. Thus, we propose Super4DR, a 4D radar-centric framework for learning-based odometry estimation and gaussian-based map optimization. First, we design a cluster-aware odometry network that incorporates object-level cues from the clustered radar points for inter-frame matching, alongside a hierarchical self-supervision mechanism to overcome outliers through spatio-temporal consistency, knowledge transfer, and feature contrast. Second, we propose using 3D gaussians as an intermediate representation, coupled with a radar-specific growth strategy, selective separation, and multi-view regularization, to recover blurry map areas and those undetected based on image texture. Experiments show that Super4DR achieves a 67% performance gain over prior self-supervised methods, nearly matches supervised odometry, and narrows the map quality disparity with LiDAR while enabling multi-modal image rendering.
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