  While visual localization or SLAM has witnessed great progress in past
decades, when deploying it on a mobile robot in practice, few works have
explicitly considered the kinematic (or dynamic) constraints of the real
robotic system when designing state estimators. To promote the practical
deployment of current state-of-the-art visual-inertial localization algorithms,
in this work we propose a low-cost kinematics-constrained localization system
particularly for a skid-steering mobile robot. In particular, we derive in a
principle way the robot's kinematic constraints based on the instantaneous
centers of rotation (ICR) model and integrate them in a tightly-coupled manner
into the sliding-window bundle adjustment (BA)-based visual-inertial estimator.
Because the ICR model parameters are time-varying due to, for example,
track-to-terrain interaction and terrain roughness, we estimate these kinematic
parameters online along with the navigation state. To this end, we perform
in-depth the observability analysis and identify motion conditions under which
the state/parameter estimation is viable. The proposed kinematics-constrained
visual-inertial localization system has been validated extensively in different
terrain scenarios.
