Abstract In the global world of robotics, robots have missions to achieve interactively with human environments and online learning. Practically, for null error of a robot’s achievement, robotics systems should be provided with minimal certain information in advance. This is crucial for any high-performance robotics systems. For instance, a medical assistive robot in a medical operation room has to be able to learn as precisely as possible about tissues. In the last few years, visual simultaneous localization and mapping (VSLAM) has become an open rich area in mobile robotics research for developing truly autonomous robots. VSLAM aims to estimate simultaneously the robot pose and 3D structure of the scene through a set of matched correspondences and features extracted from multiple images. To increase efficiency, the majority of online VSLAM algorithms use Kalman filter (KF) which is a Gaussian Bayesian filter, to merge the uncertainties in the Cartesian motion and observation model. This chapter concentrates on stereo vision noise source that results in 3D reconstruction of the scene, strategies for image filtering, and feature extraction. Modern and advanced techniques, for example, KF, extended Kalman filter (EKF), and unscented Kalman filter (UKF), will be presented in detail with practical examples in the field of robotics vision research.
UKF-Based Image Filtering and 3D Reconstruction
Machine Vision and Navigation ; 267-289
2019-10-01
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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