Multiple-model RANSAC for ego-motion estimation in highly dynamic environments

Abstract

Robust ego-motion estimation in urban environments is a key prerequisite for making a robot truly autonomous, but is not easily achievable as there are two motions involved: the motions of moving objects and the motion of the robot itself. We proposed a random sample consensus (RANSAC) based ego-motion estimator to deal with highly dynamic environments using one planar laser scanner. Instead of directly sampling on individual measurements, the RANSAC process is performed at a higher level abstraction for systematic sampling and computational efficiency. We proposed a multiple-model approach to solve the problems of ego-motion estimation and moving object detection jointly in a RANSAC paradigm. To accommodate RANSAC to multiple models - a static environment model for ego-motion estimation and a moving object model for moving object detection, a compact representation models moving object information implicitly is proposed. Moving objects are successfully detected without incorporating any grid maps, that are inherently time and space consuming. The experimental results show that accurate identification of static environments can help classification of moving objects, whereas discrimination of moving objects also yields better ego-motion estimation, particularly in environments containing a significant percentage of moving objects.