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 is performed at a higher level abstraction for systematic sampling and computational efficiency. We proposed a multiplemodel 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 incorpora...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Shao-Wen Yang, Chieh-Chih Wang
• Keywords: Ego-motion Estimation, ICRA 2009, Object Detection, Robotics, Robust Ego-motion Estimation