FD-SLAM: 基于频域闭环和位姿图优化的快速密集雷达-惯性SLAM
FD-SLAM: Fast Dense Radar-Inertial SLAM with Frequency-Domain Loop Closure and Pose Graph Optimization
AI总结：
提出FD-SLAM，通过频域闭环检测和位姿图优化提升密集雷达-惯性SLAM的精度与鲁棒性，在公开数据集上达到先进水平。
摘要：
雷达SLAM对于在视觉退化环境中运行的自主地面车辆具有吸引力，然而扫描雷达噪声大、扫描速率低，且其测量值难以在长轨迹上可靠匹配。本文提出FD-SLAM，一种快速密集雷达-惯性SLAM系统，它通过频域闭环检测和位姿图优化扩展了密集雷达-惯性里程计。所提方法通过使用紧凑的频域极坐标描述符进行闭环候选检索，以及基于时间滤波、相位相关筛选、扫描对齐相似性和几何一致性检查的多阶段验证流水线，保留了扫描雷达测量的类图像结构。验证后的闭环作为非顺序约束添加到SE(2)位姿图中，与雷达-惯性里程计因子一起。FD-SLAM在公开数据集上使用标准KITTI评估指标进行评估。结果表明，FD-SLAM改进了FD-RIO基线，与当前最先进的雷达SLAM方法相比具有竞争性能，并在多个评估的驾驶轨迹上提供了良好的旋转精度。运行时分析进一步表明，在仅CPU的设置下，雷达-惯性前端运行速度高于雷达采样率，而闭环检测和图优化适合并行后台执行。
英文摘要：
Radar SLAM is attractive for autonomous ground vehicles operating in visually degraded environments, however, scanning radars are noisy, have low scanning rates, and their measurements are challenging to match reliably over long trajectories. This paper presents FD-SLAM, a fast dense radar-inertial SLAM system that extends dense radar-inertial odometry with frequency-domain loop closure and pose graph optimization. The proposed method preserves an image-like structure of scanning radar measurements by using a compact frequency-domain polar descriptor for loop-candidate retrieval and a multi-stage verification pipeline based on temporal filtering, phase-correlation screening, scan-alignment similarity, and geometric consistency checks. Verified loop closures are added as non-sequential constraints in an SE(2) pose graph together with radar-inertial odometry factors. FD-SLAM is evaluated on a publicly available dataset using standard KITTI evaluation metrics. The results show that FD-SLAM improves FD-RIO baseline, achieves competitive performance against current state-of-the-art radar SLAM methods, and provides favorable rotational accuracy across multiple evaluated driving trajectories. Runtime analysis further indicates that the radar-inertial front-end operates above the radar sampling rate on a CPU-only setup, while loop closure detection and graph optimization remain suitable for parallel background execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
