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A reliable self-contained navigation system is essential for autonomousvehicles. Based on our previous study on Wheel-INS \cite{niu2019}, awheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR)system, in this paper, we propose a multiple IMUs-based DR solution for thewheeled robots. The IMUs are mounted at different places of the wheeledvehicles to acquire various dynamic information. In particular, at least oneIMU has to be mounted at the wheel to measure the wheel velocity and takeadvantages of the rotation modulation. The system is implemented through adistributed extended Kalman filter structure where each subsystem(corresponding to each IMU) retains and updates its own states separately. Therelative position constraints between the multiple IMUs are exploited tofurther limit the error drift and improve the system robustness. Particularly,we present the DR systems using dual Wheel-IMUs, one Wheel-IMU plus one vehiclebody-mounted IMU (Body-IMU), and dual Wheel-IMUs plus one Body-IMU as examplesfor analysis and comparison. Field tests illustrate that the proposed multi-IMUDR system outperforms the single Wheel-INS in terms of both positioning andheading accuracy. By comparing with the centralized filter, the proposeddistributed filter shows unimportant accuracy degradation while holdssignificant computation efficiency. Moreover, among the three multi-IMUconfigurations, the one Body-IMU plus one Wheel-IMU design obtains the minimumdrift rate. The position drift rates of the three configurations are 0.82\%(dual Wheel-IMUs), 0.69\% (one Body-IMU plus one Wheel-IMU), and 0.73\% (dualWheel-IMUs plus one Body-IMU), respectively.

Wheel-INS2: Multiple MEMS IMU-based Dead Reckoning System for Wheeled Robots with Evaluation of Different IMU Configurations