Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 6; no. 3; pp. 4782 - 4789
Main Authors: Kilic, Cagri, Ohi, Nicholas, Gu, Yu, Gross, Jason
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Dead reckoning
Estimation
Extraterrestrial measurements
Field tests
Gaussian process
Inertial navigation
Kernel
Localization
Location awareness
Navigation systems
Planetary rovers
Reliability aspects
Rough terrain
Slippage
Space robotics and automation
Space vehicles
Training
Wheels
zero velocity update
Planetary Rovers
Localization
Space Robotics and Automation
Zero Velocity Update
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of <inline-formula><tex-math notation="LaTeX">{\sim}97{\bf \%}</tex-math></inline-formula> over 650 m drives on rough terrain.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3068893