Advancement in Visual SLAM: Feature, Object Detection and 3D Scene Understanding

Recent advancements in SLAM systems have made significant progress in terms of performance, accuracy, and efficiency. Particularly, Visual SLAM, a type of SLAM that utilizes cameras to perform simultaneous localization and mapping, offers advantages such as cost reduction in hardware and the ability to leverage various visual information. However, Visual SLAM still faces challenges, such as lighting variations, dynamic objects, rapid camera movements, and environments with limited texture or complex structures. In this paper, we introduce efforts aimed at addressing these challenges. We present feature-based methods that utilize various features for feature extraction. Object-based methods are discussed, focusing on identifying dynamic objects and static environments to enhance accuracy. We explore research from the perspective of 3D scene understanding and representation, which involves analyzing images to comprehend 3D space.