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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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LiDAR-Stabilised GNSS-IMU Platform Pose Tracking.

Timothy D'Adamo1, Tyson Phillips1, Peter McAree1

  • 1School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

Estimating a moving platform's pose using Global Navigation Satellite Systems (GNSS) and an inertial measurement unit (IMU) is improved by adding LiDAR. This sensor fusion enhances rotational accuracy, crucial for automation applications.

Keywords:
GNSSIMULiDARnavigation systemperceptionpose estimationterrain mapping

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Area of Science:

  • Robotics and Automation
  • Navigation Systems
  • Sensor Fusion

Background:

  • Estimating the six degree-of-freedom pose of moving platforms is vital for automation.
  • Current methods often fuse Global Navigation Satellite Systems (GNSS) with Inertial Measurement Units (IMU).
  • A key challenge is accurately estimating rotation around the line connecting two GNSS antennas.

Purpose of the Study:

  • To develop and evaluate a pose estimation method incorporating LiDAR to improve rotational accuracy.
  • To address the limitations of GNSS-IMU systems in estimating specific rotational degrees of freedom.
  • To validate the performance enhancement using LiDAR measurements.

Main Methods:

  • Formulation of a Kalman filter-based estimator using two GNSS receivers and one IMU.
  • Extension of the GNSS-aided IMU approach to include LiDAR measurements.
  • Performance evaluation by comparing predicted and actual LiDAR range measurements.

Main Results:

  • Augmenting a GNSS-aided IMU pose estimator with LiDAR significantly improves accuracy.
  • Mean terrain point-to-model error reduced from 0.27m to 0.06m with LiDAR integration.
  • Accuracy slightly decreased to 0.14m mean error in unknown environments.

Conclusions:

  • LiDAR integration effectively stabilizes the challenging rotational degree of freedom in GNSS-IMU pose estimation.
  • The proposed method offers a robust solution for precise platform pose estimation in various environments.
  • The enhanced accuracy is critical for advancing autonomous system capabilities.