Mobile robot guidance using adaptive event-based pose estimation and camera sensor

We present the application of an event-based state estimator to the guidance of a mobile robot. The control feedback loop is closed by means on an estimator based on an Unscented Kalman Filter (UKF) with event-based updates. The estimator requests measurements from a camera sensor only when it needs them. The measurements are triggered by an adaptive condition on the pose estimation error covariance matrix. The main advantage of this method is that it allows for reducing the volume of CPU intensive image processing which is characteristic of these kind of sensors, as well as avoiding the unnecessary overload of the communication channel. The main contribution of the current work is the actual implementation with a P3-DX robotic unit remotely controlled. We discuss practical aspects of the implementation and provide performance results for different parameter settings.