Abstract:

Moving object detection is a key point in dynamic environment perception. Occupancy grid mapping is a practical approach for the dynamic environment perception. Different moving object detection methods in the occupancy grid map are proposed aiming at four uncertain reasoning algorithms in the Bayesian framework and the evidential framework. The moving object detection methods are comparably analyzed by simulation, and they are also tested on real range data of the outdoor dynamic environment acquired by a multilayer laser scanner. Experiment results show that, by comparing with the local map at the current moment and the global map at the former moment, the original Bayesian inference and the classical Dempster’s rule of combination are able to clearly and wholly detect the moving objects, and filter out the measurement noise in static objects and free space.