The NavINST Dataset for Multi-Sensor Autonomous Navigation

TL;DR

The NavINST dataset addresses the need for robust, high-precision localization and multimodal fusion in autonomous navigation by providing a comprehensive, ROS-integrated data collection platform. It combines a rich sensor suite (IMUs, GNSS, monocular and stereo cameras, LiDARs, and four 4D ESR radars) with high-quality ground truth and indoor 3D maps, across urban and indoor trajectories. The paper details the multisensory platform, data collection and synchronization framework, ground-truth generation, calibration procedures, data organization, and demonstrations across lidar, radar, camera, and inertial modalities. By offering rich, multi-sensor data and open tooling, NavINST aims to accelerate development and evaluation of high-precision positioning, navigation, and mapping algorithms for autonomous vehicles.

Abstract

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.

Figures (20)

• Figure 1: navinst multisensory platform.
• Figure 2: Sensors and their coordinate systems.
• Figure 3: Schematic representation of the field of view of perception sensors.
• Figure 4: Point cloud examples.
• Figure 5: Omnipresense OPS241-A mounted on the front bumper.