Abstract
Rapid development of artificial intelligence motivates researchers to expand the capabilities of intelligent and autonomous robots. In many robotic applications, robots are required to make planning decisions based on perceptual information to achieve diverse goals in an efficient and effective way. The planning problem has been investigated in active robot vision, in which a robot analyzes its environment and its own state in order to move sensors to obtain more useful information under certain constraints. View planning, which aims to find the best view sequence for a sensor, is one of the most challenging issues in active robot vision. The quality and efficiency of view planning are critical for many robot systems and are influenced by the nature of their tasks, hardware conditions, scanning states, and planning strategies. In this paper, we first summarize some basic concepts of active robot vision, and then review representative work on systems, algorithms and applications from four perspectives: object reconstruction, scene reconstruction, object recognition, and pose estimation. Finally, some potential directions are outlined for future work.
Highlights
Active robot vision [1] refers to the capability of a robot that can actively adjust its visual sensors to obtain useful information for various tasks
In addition to common characteristics shared by different view planning algorithms, we summarize specific view planning algorithms designed for each of the above four applications
We summarize the characteristics of view planning algorithms targeting four applications: object reconstruction, scene reconstruction, object recognition, and pose estimation, and discuss some representative works to explain how view planning algorithms are applied to these specific tasks
Summary
Active robot vision [1] refers to the capability of a robot that can actively adjust its visual sensors to obtain useful information for various tasks. The related ideas of view planning [2–4], sensor planning [5–7], or next-best view (NBV) determination [8–10], play an important role in active vision. They enable robot vision systems to process and analyze current information to progressively cover or detect target objects (Fig. 1). Uncertainty about the environment surrounding the robot, variability of task requirements, imprecision of motion, and unreliability of visual perception are four key factors that hinder accurate perception for view planning [20, 21] Despite these challenges, the development of active robot vision continues.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
