Research on key frame selection strategy in visual semantic SLAM

• 1、School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：Visual-based simultaneous localization and map Construction (Visual SLAM) is an important research field in computer science, and it is an important technology in the fields of unmanned driving, environmental perception, robotics, etc. In recent years, with the rapid development of deep learning, semantic segmentation, as one of its core derivative technologies, has expanded a very wide range of application scenarios, providing pixel-level image understanding for human beings. In order to combine semantic segmentation with visual SLAM and explore the application of semantic segmentation in visual SLAM, based on ORBSLAM2 and SegNet semantic segmentation networks, this paper discusses and proposes a key frame selection strategy that can meet the requirements of real-time semantic information acquisition in semantic SLAM. Through the semantic delay performance test, the results show that the improved selection strategy can ensure that the semantic key frame information used is close to the current tracking frame, and the delay performance is better than the traditional sequential key frame selection strategy.

Keywords： software engineering visual slam semantic slam key frame semantic segmentation