Motion clustering and estimation with conditional random fields

• Published in: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 872 - 877
• Main Authors: Tipaldi, G.D., Ramos, F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2009
• Subjects: Intelligent robots; Iterative closest point algorithm; Laser tuning; Motion detection; Motion estimation; Object detection; Robot motion; Robot sensing systems; Safety; USA Councils
• ISBN: 9781424438037; 1424438039
• ISSN: 2153-0858
• DOI: 10.1109/IROS.2009.5354692

Moving objects are present in many robotic applications. An accurate detection and motion estimation of these objects can be crucial for the success and safety of the robot and people surrounding it. This paper presents a new probabilistic framework for clustering dependent or relational data, applied to the problem of motion clustering and estimation. While conventional techniques such as scan differencing perform well in many cases, they usually assume that a good pose estimate is available and fail when points belonging to dynamic objects show some overlap in consecutive readings. The technique proposed, CRF-Clustering, by explicitly reasoning about the underlying motion of the object, is able to deal with poor initial motion estimate and overlapping points. Moreover, it is able to consider the dependencies between neighbor points in the scans to reduce the noise in the clustering assignment. The model parameters can be estimated from labeled data in a statistically sound learning procedure. Experiments show that CRF-Clustering is able to detect moving objects, cluster them and estimate their motion.