RANSAC matching: Simultaneous registration and segmentation

The iterative closest points (ICP) algorithm is widely used for ego-motion estimation in robotics, but subject to bias in the presence of outliers. We propose a random sample consensus (RANSAC) based algorithm to simultaneously achieving robust and realtime ego-motion estimation, and multiscale segmentation in environments with rapid changes. Instead of directly sampling on measurements, RANSAC matching ates initial estimates at the object level of abstraction for systematic sampling and computational efficiency. A soft segmentation method using a multi-scale representation is exploited to eliminate segmentation errors. By explicitly taking into account the various noise sources degrading the effectiveness of geometric alignment: sensor noise, dynamic objects and data association uncertainty, the uncertainty of a relative pose estimate is calculated under a theoretical investigation of scoring in the RANSAC paradigm. The improved segmentation can also be used as the basis for higher le...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Shao-Wen Yang, Chieh-Chih Wang, Chun-Hua Chang
• Keywords: Ego-motion Estimation, ICRA 2010, Iterative Closest Points, Robotics, Soft Segmentation Method