Reactive Visual Odometry Scheduling Based on Noise Analysis using an Adaptive Extended Kalman Filter

A new strategy is proposed for scheduling Visual Odometry (VO) measurements for wheeled ground vehicles. Rather than having a fixed interval or distance between image acquisitions, we propose to trigger VO based on covariances from an Adaptive Extended Kalman Filter. The adopted model uses process noise to drive wheel slip estimation, which, when correctly identified, can be used with Wheel Odometry to provide frequent position estimates. When more dynamic terrain is detected, more VO measurements are scheduled to maintain localization accuracy. On the other hand, when the terrain is stable, VO usage is limited. The system is validated in a simple one-dimensional case using data captured during field trials using a representative rover. The results are promising as trajectories that were subjected to large errors are corrected.