Prediction of moving objects in dynamic environments using Kalman filters

In this work, we describe a framework for predicting future positions and orientation of moving obstacles in a time-varying environment using Kalman filtering techniques. No constraint are placed on the obstacles motion. The proposed algorithm can be used in a variety of applications, one of which is robot motion planning in time varying environments. The advantage of using this model compared to reported ones in the literature is it's ability to start the prediction process from the first time step without the need to wait for few time steps before starting the prediction process. Early experimental results are very encouraging.