Progress and Applications of Visual SLAM

Abstract

Visual simultaneous localization and mapping (SLAM) provides mapping and self-localization results for a robot in an unknown environment based on visual sensors, that have the advantages of small volume, low power consumption, and richness of information acquisition. Visual SLAM is essential and plays a significant role in supporting automated and intelligent applications of robots. This paper presents the key techniques of visual SLAM, summarizes the current research status, and analyses the new trends of visual SLAM research and development. Finally, specific applications of visual SLAM in restricted environments, including deep space and indoor scenarios, are discussed.

Keywords

• |visual slam|feature extraction|kalman filter|graph based optimization|loop closure detection