Abstract
This paper reviews the application progress of Simultaneous localization and mapping (SLAM) in complex indoor environments. SLAM is a technology used to manufacture mobile robots and autonomous vehicle. It can achieve autonomous localization and mapping in unknown environments. In dealing with complex indoor environments, SLAM technology faces many challenges, such as missing local perspectives, detecting moving objects, constructing dense maps, and real-time requirements in large-scale environments. However, with the development of technology, more and more SLAM technologies are being applied to complex indoor environments, among which the fusion of multimodal vision and deep learning technology, enhanced camera positioning technology, and navigation algorithms based on intelligent platforms are currently relatively advanced technologies. This article will briefly introduce the main development process of SLAM technology in dealing with complex indoor environments and the application of deep learning technology. By utilizing this technology, map features can be extracted more accurately, objects can be recognized, and obstacle avoidance can be achieved. This provides a good development direction for SLAM technology.
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