4D radars are increasingly favored for odometry and mapping of autonomous systems due to their robustness in harsh weather and dynamic environments. Existing datasets, however, often cover limited areas and are typically captured using a single platform. To address this gap, we present a diverse large-scale dataset specifically designed for 4D radar-based localization and mapping. This dataset was gathered using three different platforms: a handheld device, an e-bike, and an SUV, under a variety of environmental conditions, including clear days, nighttime, and heavy rain. The data collection occurred from September 2023 to February 2024, encompassing diverse settings such as roads in a vegetated campus and tunnels on highways. Each route was traversed multiple times to facilitate place recognition evaluations. The sensor suite included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a GNSS/INS system. Sensor data packets were synchronized to GNSS time using a two-step process: a convex hull algorithm was applied to smooth host time jitter, and then odometry and correlation algorithms were used to correct constant time offsets. Extrinsic calibration between sensors was achieved through manual measurements and subsequent nonlinear optimization. The reference motion for the platforms was generated by registering lidar scans to a terrestrial laser scanner (TLS) point cloud map using a lidar inertial odometry (LIO) method in localization mode. Additionally, a data reversion technique was introduced to enable backward LIO processing. We believe this dataset will boost research in radar-based point cloud registration, odometry, mapping, and place recognition.

翻译：4D雷达因其在恶劣天气和动态环境中的鲁棒性，在自主系统的里程计与建图任务中日益受到青睐。然而，现有数据集通常覆盖区域有限，且多由单一平台采集。为弥补这一不足，我们提出了一个专为基于4D雷达的定位与建图设计的大规模多样化数据集。该数据集使用三种不同平台采集：手持设备、电动自行车和SUV，涵盖多种环境条件，包括晴天、夜间和暴雨。数据采集时间为2023年9月至2024年2月，覆盖多样化场景，如植被茂密的校园道路和高速公路隧道。每条路线均被多次遍历，以支持地点识别评估。传感器套件包括3D激光雷达、4D雷达、立体相机、消费级IMU以及GNSS/INS系统。传感器数据包通过两步流程与GNSS时间同步：首先应用凸包算法平滑主机时间抖动，随后使用里程计与相关算法校正恒定时间偏移。传感器间的外参标定通过人工测量及后续非线性优化实现。平台的参考运动通过将激光雷达扫描点云与地面激光扫描仪点云地图配准生成，该过程采用定位模式下的激光雷达惯性里程计方法。此外，我们引入了一种数据反转技术以实现逆向LIO处理。我们相信该数据集将有力推动基于雷达的点云配准、里程计、建图及地点识别领域的研究。