首个融合多传感器的城市场景自动驾驶数据集，支持高精度定位与算法验证。
The NavINST Dataset for Multi-Sensor Autonomous Navigation
- 采集城市道路多种光照下的多传感器数据，含固态/机械激光雷达
- 配备商业级与战术级惯性测量单元，提供高精度里程计与GNSS/IMU融合定位
- 兼容ROS系统，适合自动驾驶、多源融合与高精地图研究者使用
NavINST实验室构建了一个面向城市环境的多传感器综合数据集，涵盖多种光照条件下的道路测试轨迹，包括室内车库场景及密集3D地图。数据集包含多个商用级和一台高端战术级惯性测量单元（IMU），以及多种感知传感器：固态激光雷达（首次在该类数据集中出现）、机械激光雷达、四台电子扫描雷达、单目相机和两套立体相机。此外，还提供基于车辆里程计的前向速度数据，以及经后处理的高精度GNSS/IMU融合定位信息，作为精确的真值导航数据。该数据集旨在支持高精度定位、导航、建图、计算机视觉及多传感器融合等前沿研究，具备丰富多源数据特征，适用于开发与验证自动驾驶鲁棒算法。数据集完整集成于ROS平台，便于科研社区使用。完整数据与开发工具可访问 https://navinst.github.io。
原文摘要 · Abstract (English)
The NavINST Laboratory has developed a comprehensive multisensory dataset from various road-test trajectories in urban environments, featuring diverse lighting conditions, including indoor garage scenarios with dense 3D maps. This dataset includes multiple commercial-grade IMUs and a high-end tactical-grade IMU. Additionally, it contains a wide array of perception-based sensors, such as a solid-state LiDAR - making it one of the first datasets to do so - a mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and two stereo cameras. The dataset also includes forward speed measurements derived from the vehicle's odometer, along with accurately post-processed high-end GNSS/IMU data, providing precise ground truth positioning and navigation information. The NavINST dataset is designed to support advanced research in high-precision positioning, navigation, mapping, computer vision, and multisensory fusion. It offers rich, multi-sensor data ideal for developing and validating robust algorithms for autonomous vehicles. Finally, it is fully integrated with the ROS, ensuring ease of use and accessibility for the research community. The complete dataset and development tools are available at https://navinst.github.io.
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