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Research on Fusion SLAM of Binocular Camera and Lidar(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年01期
Page:
64-71
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Fusion SLAM of Binocular Camera and Lidar
Author(s):
Liu Hongxun; Wang Wei
College of Automation,Nanjing University of Information Science and Technology,Nanjing 210006,China
Keywords:
FAST feature points; graph optimized; SLAM; binocular vision; bag of words
PACS:
TP242.6
DOI:
10.3969/j.issn.1672-1292.2021.01.010
Abstract:
Aiming at the problem of closed-loop detection errors in the graph-optimized laser SLAM algorithm in high-similarity scenes,the method of using binocular cameras for closed-loop detection is presented in this paper. We use FAST feature points and BRIEF descriptors with rotation invariance for binocular depth estimation. We introduce the concept of local maps,and use single-frame lidar data to match with local maps to improve the accuracy of the SLAM front-end. A k-ary tree dictionary based on the BOW model is used to evaluate the similarity of the pictures to complete the closed-loop detection,and finally,the global optimization problem is constructed and solved. The contrastive experiment with the mainstream open source lidar SLAM algorithms shows that the method improves the problem which the closed-loop detection method that only uses lidar data for closed-loop detection fails in a scene with high similarity,and that the operation effect in a larger area is significantly better than that based on Filtered SLAM algorithm.

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Last Update: 2021-03-15