title: Obstacle classification and 3D measurement in unstructured environments based on ToF cameras.
abstract: Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot's movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient.
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China. yuhongshancn@hotmail.com.
authors.forename: Hongshan
authors.initials: H
authors.lastname: Yu
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China. jiang126@126.com.
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhu
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China. wang_yaonan@hotmail.com.
authors.forename: Yaonan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA. jiawenyan@gmail.com.
authors.forename: Wenyan
authors.initials: W
authors.lastname: Jia
authors.affiliation: Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA. drsun@pitt.edu.
authors.forename: Mingui
authors.initials: M
authors.lastname: Sun
authors.affiliation: State Key Laboratory of Robotics, Shenyang 110016, China. ytang@sia.cn.
authors.forename: Yandong
authors.initials: Y
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000295:instrumentation / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D015348:Vision, Binocular / Q000502:physiology*