Visual-Inertial-Wheel Odometry with Online Calibration
Woosik Lee, Kevin Eckenhoff, Yulin Yang, Patrick Geneva, Guoquan Huang
Abstract
In this paper, we introduce a novel visual-inertial-wheel odometry (VIWO) system for ground vehicles, which efficiently fuses multi-modal visual, inertial and 2D wheel odometry measurements in a sliding-window filtering fashion. As multi-sensor fusion requires both intrinsic and extrinsic (spatiotemproal) calibration parameters which may vary over time during terrain navigation, we propose to perform VIWO along with online sensor calibration of wheel encoders' intrinsic and extrinsic parameters. To this end, we analytically derive the 2D wheel odometry measurement model from the raw wheel encoders' readings and optimally fuse this 2D relative motion information with 3D visual-inertial measurements. Additionally, an observability analysis is performed for the linearized VIWO system, which identifies five commonly-seen degenerate motions for wheel calibration parameters. The proposed system has been validated extensively in both Monte-Carlo simulations and real-world experiments in large-scale urban driving scenarios.
BibTeX
@inproceedings{iros2020_visualinertialwh,
title = {Visual-Inertial-Wheel Odometry with Online Calibration},
author = {Woosik Lee and Kevin Eckenhoff and Yulin Yang and Patrick Geneva and Guoquan Huang},
booktitle = {IROS 2020},
year = {2020}
}