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Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments
Benjamin J McLoughlin1, Harry A G Pointon2, John P McLoughlin3
1Engineering and Technology Research Institute, Liverpool John Moores University, 3 Byrom St, Liverpool L3 3AF, UK. b.mcloughlin@2011.ljmu.ac.uk.
Sensors (Basel, Switzerland)
|July 18, 2018
Summary
This study improves robot positioning by characterizing sensor noise using a Trimble Robotic Total Station. The enhanced localization extended Kalman filter provides more accurate and smoother robot trajectories.
Area of Science:
- Robotics
- Sensor Fusion
- Localization and Navigation
Background:
- Advancements in autonomous robotic technology necessitate precise localization systems.
- Accurate estimation of sensor measurement noise is crucial for robust robotic positioning uncertainty models.
Purpose of the Study:
- To dynamically characterize the positioning sensor error of a ground-based unmanned robot.
- To integrate these error characteristics into an improved localization algorithm.
- To validate the enhanced algorithm against a ground truth metric.
Main Methods:
- Utilized a Trimble S7 Robotic Total Station for dynamic error characterization of robot positioning sensors.
- Developed a Localization Extended Kalman Filter (LEKF) incorporating sensor error characteristics.
- Fused Pozyx Ultra-wideband (UWB) range measurements with odometry data within the LEKF.
- Employed the Robotic Total Station's remote tracking feature for ground truth trajectory generation.
Main Results:
- The proposed LEKF method demonstrated superior positional estimation accuracy compared to the Pozyx system's native firmware.
- The enhanced localization approach resulted in a significantly smoother robot trajectory.
- Dynamic sensor error characterization proved effective for improving robotic localization.
Conclusions:
- Characterizing sensor noise using surveying-grade instruments enhances robotic localization accuracy.
- The developed LEKF offers a more reliable positioning solution for autonomous robots.
- This methodology provides a pathway for more robust robot navigation in diverse environments.
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