DIDLM

Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads. Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeterwave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains. We evaluated various SLAM algorithms using this dataset, including RGB images, infrared images, depth images, LiDAR, and 4D millimeter-wave radar. The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions.

恶劣天气、低光照环境以及颠簸路面，给机器人导航与自动驾驶领域的同步定位与地图构建（Simultaneous Localization and Mapping，SLAM）带来了严峻挑战。当前该领域的现有数据集大多依赖单传感器，或是激光雷达（LiDAR）、相机与惯性测量单元（Inertial Measurement Unit，IMU）的组合方案。然而，4D毫米波雷达在恶劣天气下表现出优异的鲁棒性，红外相机擅长在低光照环境下捕捉细节信息，深度图像则能提供更为丰富的空间信息。多传感器融合方法也展现出更好适配颠簸路面场景的潜力。尽管已有部分SLAM研究引入了上述传感器与场景类型，但目前仍缺乏针对低光照环境与颠簸路面场景的综合性数据集，或是传感器数据种类不够丰富的数据集。本研究构建了一款多传感器数据集，涵盖雪天、雨天、夜间、减速带以及崎岖地形等复杂场景。该数据集纳入了极端场景下鲜有使用的传感器，包括4D毫米波雷达、红外相机与深度相机，同时搭配了3D激光雷达（LiDAR）、RGB相机、全球定位系统（Global Positioning System，GPS）以及惯性测量单元（IMU）。该数据集可同时适配自动驾驶与地面机器人应用场景，并提供可靠的GPS/惯性导航（GPS/Inertial Navigation System，GPS/INS）真值数据，覆盖结构化与半结构化地形。研究团队基于本数据集对多款SLAM算法进行了评测，涵盖RGB图像、红外图像、深度图像、激光雷达以及4D毫米波雷达等多类输入模态。该数据集总覆盖里程达18.5公里，总时长69分钟，数据总量约660GB，可为复杂与极端场景下的SLAM研究提供宝贵的实验资源。