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MUN-FRL: A Visual-Inertial-LiDAR Dataset for Aerial Autonomous Navigation and Mapping.

Ravindu G Thalagala1, Oscar De Silva1, Awantha Jayasiri2

  • 1Intelligence Systems Lab, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada.

The International Journal of Robotics Research
|April 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new aerial dataset for Global Navigation Satellite System (GNSS)-denied navigation research, featuring synchronized visual-inertial-LiDAR data from drones and helicopters. This resource aids in developing advanced autonomous navigation and perception algorithms.

Keywords:
Datasetaerial autonomydronesfull-scale aircraftvisual-inertial-LiDAR odometry and mapping

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Area of Science:

  • Robotics
  • Computer Vision
  • Aerospace Engineering

Background:

  • Global Navigation Satellite System (GNSS) is crucial for navigation but can be unreliable in certain environments.
  • Developing robust autonomous navigation systems for aerial vehicles in GNSS-denied conditions is a significant challenge.
  • Existing datasets may lack the diversity or sensor fusion richness required for advanced algorithm development.

Purpose of the Study:

  • To present a comprehensive, multi-sensor aerial dataset for advancing GNSS-denied navigation research.
  • To provide synchronized visual-inertial-LiDAR data with high-precision ground truth for algorithm development.
  • To support research in odometry, mapping, object detection, and landing zone identification for aerial robots.

Main Methods:

  • Collected data using a DJI-M600 hexacopter drone and NRC Bell412 ASRA.
  • Integrated hardware-synchronized sensors: monocular cameras, Inertial Measurement Units (IMU), and 3D Light Detection and Ranging (LiDAR).
  • Acquired high-precision Real-Time Kinematic (RTK)-GNSS ground truth for nine diverse outdoor sequences (urban, rural, waterfront).

Main Results:

  • A unique dataset comprising over 100 minutes of flight data in Robot Operating System (ROS) bag format.
  • Includes raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth.
  • Provides sensor calibration data and a performance benchmark of state-of-the-art algorithms.

Conclusions:

  • The released dataset is a valuable resource for researchers in aerial robotics and autonomous navigation.
  • Facilitates the development and validation of algorithms for GNSS-denied environments.
  • Enables advancements in visual-inertial-LiDAR odometry, mapping, and perception tasks.