Unsupervised classification of dynamic obstacles in urban environments

Abstract

This paper presents a solution to the problem of unsupervised classification of dynamic obstacles in urban environments. A track-based model is introduced for the integration of two-dimensional laser and vision information that provides a robust spatiotemporal synthesis of the sensed moving obstacles and forms the basis for suitable algorithms to perform unsupervised classification by clustering. This work presents various contributions in order to achieve accurate and efficient performance, initially using laser tracks for classification and then incorporating visual tracks to the model. A procedure is proposed for accurate unsupervised classification of dynamic obstacles using a laser stamp representation of the tracks. Laser data are then integrated with visual information through a single-instance visual stamp representation, which is finally extended using a multipleinstance framework to robustly deal with challenges associated with perception in real-world scenarios. The proposed algorithms are extensively validated with a simulated environment. Experiments with a research vehicle in an urban environment demonstrate the performance of the approach with real data. The experimental results reach an accuracy of more than 92% for obstacle classification, finding the clusters that correspond to the main obstacle classes in the data. © 2010 Wiley Periodicals, Inc.