A novel relocation method for simultaneous localization and mapping based on deep learning algorithm. (October 2017)

• Record Type: Journal Article
• Authors: Cao, Jun; Zeng, Bi; Liu, Jianqi; Zhao, Zhenting; Su, Yongfeng
• Is Part Of: Computers & electrical engineering. Volume 63(2017)
• Page Start: 79
• Page End: 90
• Publication Date: 2017-10
• Subjects: SLAM -- Relocation -- Deep learning -- Cloud robotics -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.03.015
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Relocation is one of the most common problems in Simultaneous Localization and Mapping (SLAM). This paper presents a novel relocation method, using unsupervised deep learning algorithm to extract the feature of Light Detection and Ranging (LiDAR) data, and narrows the scope of relocation by classifying these features to reduce the randomness of the relocation. Compared with the other methods which is based on matching the manual feature points, this method avoids some limitations of manual features. We modify the Particle Filter SLAM (PF-SLAM), and use our relocation method to replace the original method for experimentation. The experimental results demonstrate that this method can be relocation whit high success rate only use a small amount of computational resource in a short time. Training neural network will consume a lot of computing resources, we also propose a cloud computing framework to the implementation of this method for the mobile robot which computational resources are limited.