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.Comment: Accepted by IEEE ITSC 202