Multisensor fusion-based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather-sensitive, while the frequency-modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

中文翻译：

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城市结构环境中 LiDAR 地图定位的 FMCW 雷达

基于多传感器融合的定位技术已经在自主系统中实现了高精度。如何提高鲁棒性是目前面临的主要挑战。最常用的激光雷达和摄像头对天气敏感，而调频连续波雷达适应性强，但存在噪声和鬼影效应。在本文中，我们提出了一种名为 Radar on LiDAR Map 的异构定位方法，旨在通过实时减轻雷达里程计的累积误差来提高定位精度，而不依赖于闭环。为了实现这一目标，我们利用激光雷达扫描和地面真实路径作为教学路径，利用雷达扫描作为要估计的轨迹，称为重复路径。通过建立雷达和激光雷达扫描数据之间的相关性，我们可以提高雷达里程计估计的准确性。我们的方法包括将雷达和激光雷达传感器的数据嵌入到密度图中。我们计算带有偏移量的空间向量相似度，以确定候选地图内相应的位置索引并估计旋转和平移。为了细化对齐，我们利用迭代最近点算法来实现 LiDAR 子图上的最佳匹配。估计的偏差随后被纳入雷达 SLAM 中以优化位置图。我们对 Mulran 雷达数据集、牛津雷达 RobotCar 数据集和我们的数据集进行了广泛的实验，以证明我们提出的方法的可行性和有效性。我们提出的扫描投影描述符实现了同质和异质位置识别，并且比现有方法效果更好。其应用于雷达SLAM系统也大幅提高了定位精度。所有序列的定位均方根误差为2.53 m，角度误差为1.83°。