Wheelchair tire slip is a dangerous scenario that can result from powered wheelchair operation on icy or low-friction surfaces. This research applies instantaneous center of rotation (ICR) estimation to a wheelchair application where ICR values should rarely change. Changes in these estimates are used to understand the occurrence of slip and to predict the motion during that slip. Using inputs of position and orientation obtained through laser odometry, an extended Kalman filter (EKF) algorithm is implemented to estimate the changes in wheelchair ICR location estimates that are indicative of slippage. The algorithm is verified via simulation and experimentation using a robotic wheelchair. It is observed that the convergence of the ICR EKF is heavily dependent on the motion history of the wheelchair. Experiments show that ICR locations do not vary significantly under the slip-free conditions of normal operation with 2- σ standard deviations of 0.076 m, but the ICR locations deviate up to 0.84 m during slippage.