Active Sensor Planning for Multiview Vision Tasks - Tapa dura

Acerca del autor

Prof. Yujun Zheng received his Ph.D. degree from Institute of Software, Chinese Academy of Science in 2010, and now is Professor and Ph.D. supervisor at College of Computer Science and Technology, Zhejiang University of Technology, China. His research interests include intelligent computing and its application in operations research. He has authored over 60 scientific papers in well-known international journals and conferences. In 2014, he received the runner-up of IFORS Prize for OR Development with the work of biogeography-based optimization for emergency engineering rescue scheduling in disaster relief operations in China. Dr. Xueqin Lu is a Ph.D. candidate at College of Computer Science and Technology, Zhejiang University of Technology, China. Her main research interests are bio-inspired algorithms and their applications. Dr. Minxia Zhang is an Associate Professor at College of Computer Science and Technology, Zhejiang University of Technology, China. Her main research interests include bio-inspired computation and its applications. Dr. Shengyong Chen received his Ph.D. degree from City University of Hong Kong in 2003, and now is Professor, Director of Institute of Computer Vision, at College of Computer Science and Technology, Zhejiang University of Technology, China. His research focuses on computer vision and robotics. Prof. Chen is a recipient of National Outstanding Youth Foundation Award (NSFC) in 2014, IEEE Sensors Journal Best Paper Award in 2012, Research Fellowship of Alexander von Humboldt, Germany in 2006 and is also IET fellow, IEEE senior member.

De la contraportada

Vision sensors have limited fields of views and can only "see" a portion of a scene from a single viewpoint. To make the entire object visible, the sensor has to be moved from one place to another around the object to observe all features of interest, which brings a multiview vision task that has to be solved by means of active perception. The sensor planning presented in this book describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan. For nonmodel-based applications, the method involves determination of the best next view and sensor settings, to incrementally acquire the object information and to find geometrical cues to predict the unknown portion of an object or environment.

The ten chapters in Active Vision Planning draw on recent work in robot vision over ten years, particularly in the use of new concepts of active sensing, reconfiguration, recalibration, sensor modeling, sensing constraints, sensing evaluation, viewpoint decision, sensor placement graph, model based planning, path planning, planning for robot in unknown environment, dynamic 3D construction, surface prediction, etc. Implementation examples are also provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.