This paper presents an online approach to calibrating vehicle model parameters that uses the integrated dynamics of the system. Specifically, we describe the identification of the time constant and delay in a first-order model of the vehicle powertrain, as well as parameters required for pose estimation (including position offsets for the inertial measurement unit, steer angle sensor parameters, and wheel radius). Our approach does not require differentiation of state measurements like classical techniques; making it ideal when only low-frequency measurements are available. Experimental results on the LandTamer and Zoë rover platforms show online calibration using integrated dynamics to be fast and more accurate than both manual and classical calibration methods.

When GPS, or other absolute positioning, is un-available, terrain-relative velocity is crucial for dead reckoning and the vehicle's pose estimate. Unfortunately, the position-denied accuracy of the inertial navigation system (INS) is governed by the performance of the linear velocity aiding sources, such as wheel odometry, which are typically corrupted by large systematic errors due to wheel slip. As a result, affordable terrestrial inertial navigation is ineffective in estimating position when denied position fixes for an extended period of time. For mobile robots, the mapping between inputs and resultant behavior depends critically on terrain conditions which vary significantly over time and space which cannot be pre-programmed. Past work has used Integrated Perturbative Dynamics (IPD) to identify successively systematic and stochastic models of wheel slip, but treated the pose filter only as input without improving the odometry measurements used for vehicle navigation. We present a unique approach of a predictive vehicle slip model in a delayed state extended Kalman filter. The relative pose difference between the current state and delayed state is used as a measurement update to the vehicle slip model. These results create an opportunity to compensate for wheel slip effects in terrestrial inertial navigation. This paper presents the design, calibration, and verification of such a system and concludes that the position-denied performance of the compensated system is far superior.