GPS-aided Visual Wheel Odometry

TL;DR

This paper presents a GPS-aided visual-wheel odometry system that fuses visual, wheel encoder, and GPS data using MSCKF, with online calibration of extrinsic parameters, achieving improved accuracy in urban driving scenarios.

Contribution

The paper introduces a novel GPS-VWO system with online extrinsic calibration and theoretical analysis of unobservable state variance convergence.

Findings

Achieves better accuracy than GPS alone.

Online calibration significantly improves localization.

System verified in large-scale urban scenarios.

Abstract

This paper introduces a novel GPS-aided visual-wheel odometry (GPS-VWO) for ground robots. The state estimation algorithm tightly fuses visual, wheeled encoder and GPS measurements in the way of Multi-State Constraint Kalman Filter (MSCKF). To avoid accumulating calibration errors over time, the proposed algorithm calculates the extrinsic rotation parameter between the GPS global coordinate frame and the VWO reference frame online as part of the estimation process. The convergence of this extrinsic parameter is guaranteed by the observability analysis and verified by using real-world visual and wheel encoder measurements as well as simulated GPS measurements. Moreover, a novel theoretical finding is presented that the variance of unobservable state could converge to zero for specific Kalman filter system. We evaluate the proposed system extensively in large-scale urban driving scenarios. The results demonstrate that better accuracy than GPS is achieved through the fusion of GPS and VWO. The comparison between extrinsic parameter calibration and non-calibration shows significant improvement in localization accuracy thanks to the online calibration.