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.

翻译：雷达SLAM对于在视觉退化环境下运行的地面自主车辆具有吸引力，然而扫描雷达存在噪声大、扫描速率低以及难以在长轨迹上可靠匹配测量值等挑战。本文提出FD-SLAM，这是一种通过频域闭环检测与位姿图优化增强密集雷达-惯性里程计的快速密集雷达-惯性SLAM系统。所提方法通过采用紧凑的频域极坐标描述符进行闭环候选检索，并构建基于时间滤波、相位相关筛选、扫描对齐相似度及几何一致性校验的多阶段验证流水线，从而保留扫描雷达测量数据的类图像结构。经验证的闭环作为非序列约束，与雷达-惯性里程计因子共同融入SE(2)位姿图。基于标准KITTI评估指标在公开数据集上的实验表明，FD-SLAM在提升FD-RIO基线性能的同时，与当前最先进的雷达SLAM方法相比具有竞争力，并在多个测试驾驶轨迹中展现出优异的旋转精度。运行时分析进一步表明，在仅使用CPU的配置下，雷达-惯性前端处理速率超过雷达采样率，而闭环检测与图优化过程适合作为并行后台任务运行。