Developing visual sensing strategies through next best view planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
View planning for 3D object reconstruction
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FGN based telecommunication traffic models
WSEAS Transactions on Computers
Perspective imaging under structured light
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Optimal view path planning for visual SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
An autonomous six-DOF eye-in-hand system for in situ 3D object modeling
International Journal of Robotics Research
On the use of depth camera for 3D phenotyping of entire plants
Computers and Electronics in Agriculture
Next-best-view planning for 3d object reconstruction under positioning error
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Information-gain view planning for free-form object reconstruction with a 3d ToF camera
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Covariance propagation and next best view planning for 3d reconstruction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
An efficient method for fully automatic 3D digitization of unknown objects
Computers in Industry
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An active robot system can change its visual parameters in an intentional manner and perform its sensing actions purposefully. A general vision task thus can be performed in an efficient way by means of strategic control of the perception process. The controllable processes include 3D active sensing, sensor configuration and recalibration, automatic sensor placement, and 3D sensing. This book explores these important issues in studying for active visual perception. 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. The sensor planning presented in this book describes an effective strategy 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. A topology of viewpoints is achieved by a genetic algorithm in which a min-max criterion is used for evaluation. A shortest path is also determined by graph algorithms. For nonmodel-based applications, the method involves determination of the best next view and sensor settings. The trend surface is proposed as the cue to predict the unknown portion of an object or environment. The 11 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 model, 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.