Robot Tracking Navigation Based on Visual SLAM

Abstract

Visual Simulation Localization and Mapping technology is an important research direction in the fields of computer vision and robotics. The research background of this technology mainly stems from the insufficient accuracy of traditional positioning and map construction methods in the face of environmental changes. With the development of artificial intelligence, this technology has been involved in multiple disciplinary fields. Since 2015, researchers have begun to focus on the combination with deep learning to improve algorithm robustness, dynamic scene planning and other aspects. This article which introduces the existing tracking car system and visual SLAM (Simultaneous Localization and Mapping) system framework explores the robot tracking and navigation technology based on visual SLAM. Visual SLAM technology provides robots with an autonomous navigation solution that does not require external sensors through feature extraction, real-time positioning, mapping, and closed-loop detection. This article also discusses the advantages and disadvantages of visual SLAM navigation, and offers insights into the future development of VSLAM technology in robot tracking navigation.