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.

翻译：本文提出了一种新颖的GPS辅助视觉轮式里程计（GPS-VWO），用于地面机器人状态估计。该估计算法以多状态约束卡尔曼滤波（MSCKF）方式紧密融合视觉、轮式编码器和GPS测量值。为避免校准误差随时间累积，所提算法将GPS全局坐标系与VWO参考坐标系之间的外部旋转参数作为估计过程的一部分进行在线计算。通过可观测性分析确保该外部参数的收敛性，并利用真实世界的视觉和轮式编码器测量值以及模拟GPS测量值进行验证。此外，本文提出了一项新颖的理论发现：对于特定卡尔曼滤波系统，不可观测状态的方差可能收敛至零。我们在大规模城市驾驶场景中对该系统进行了广泛评估。结果表明，通过GPS与VWO的融合，定位精度优于单独使用GPS。外部参数在线校准与非校准的对比显示，在线校准显著提升了定位精度。