Robust Preintegrated Wheel Odometry for Off-Road Autonomous Ground Vehicles

Wheel odometry is not often used in state estimation for off-road vehicles due to frequent wheel slippage, varying wheel radii, and the 3D motion of the vehicle not fitting with the 2D nature of integrated wheel odometry. This letter attempts to overcome these issues by proposing a novel 3D preintegration of wheel encoder measurements on manifold. Our method additionally estimates wheel slip, radii, and baseline online to improve accuracy and robustness. Further, due to the preintegration, many measurements can be summarized into a single motion constraint using first-order updates for wheel slippage and intrinsics, allowing for efficient usage in an optimization-based state estimation framework. While our method can be used with any sensors in a factor graph framework, we validate its effectiveness and observability of parameters in a vision-wheel-odometry system (VWO) in a Monte Carlo simulation. Additionally, we illustrate its accuracy and demonstrate it can be used to overcome other sensor failures in real-world off-road scenarios in both a VWO and visual-inertial-wheel odometry (VIWO) system.