Deep Learning for Visual SLAM in Transportation Robotics: A review. Issue 3 (30th January 2020)

• Record Type: Journal Article
• Authors: Duan, Chao; Junginger, Steffen; Huang, Jiahao; Jin, Kairong; Thurow, Kerstin
• Is Part Of: Transportation safety and environment. Volume 1:Issue 3(2019)
• Page Start: 177
• Page End: 184
• Publication Date: 2020-01-30
• Subjects: deep learning -- visual SLAM -- transportation robotics -- mobile robots; Transportation engineering -- Periodicals; Transportation -- Safety measures -- Periodicals; 629.04
• DOI: 10.1093/tse/tdz019
• Languages: English
• ISSNs: 2631-4428

Abstract

Abstract: Visual SLAM (Simultaneously Localization and Mapping) is a solution to achieve localization and mapping of robots simultaneously. Significant achievements have been made during the past decades, geography-based methods are becoming more and more successful in dealing with static environments. However, they still cannot handle a challenging environment. With the great achievements of deep learning methods in the field of computer vision, there is a trend of applying deep learning methods to visual SLAM. In this paper, the latest research progress of deep learning applied to the field of visual SLAM is reviewed. The outstanding research results of deep learning visual odometry and deep learning loop closure detect are summarized. Finally, future development directions of visual SLAM based on deep learning is prospected.