The NavINST Dataset for Multi-Sensor Autonomous Navigation

This is a comprehensive multi-sensory dataset from various road trajectories in Kingston, ON, and Calgary, AB, Canada. This dataset features diverse lighting conditions and includes data from two indoor garages, with corresponding 3D maps. The dataset includes data from multiple commercial-grade IMUs and a tactical-grade IMU, along with a wide range of perception sensors. Notably, it is one of the first datasets to feature a solid-state LiDAR, alongside a mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and two stereo cameras. Additionally, the dataset contains forward speed measurements from the vehicle's odometer and accurately post-processed high-end GNSS/IMU data, providing precise ground truth positioning and navigation information. The NavINST dataset is designed to facilitate advanced research in high-precision positioning, navigation, mapping, computer vision, and multi-sensor fusion, offering rich data for developing and validating robust autonomous vehicle algorithms. Finally, the dataset is fully integrated with ROS to ensure ease of use and accessibility for the research community. It also includes a toolkit for users working offline.