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AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System

Authors :
Xu, Kuan
Hao, Yuefan
Yuan, Shenghai
Wang, Chen
Xie, Lihua
Publication Year :
2024

Abstract

In this paper, we present an efficient visual SLAM system designed to tackle both short-term and long-term illumination challenges. Our system adopts a hybrid approach that combines deep learning techniques for feature detection and matching with traditional backend optimization methods. Specifically, we propose a unified convolutional neural network (CNN) that simultaneously extracts keypoints and structural lines. These features are then associated, matched, triangulated, and optimized in a coupled manner. Additionally, we introduce a lightweight relocalization pipeline that reuses the built map, where keypoints, lines, and a structure graph are used to match the query frame with the map. To enhance the applicability of the proposed system to real-world robots, we deploy and accelerate the feature detection and matching networks using C++ and NVIDIA TensorRT. Extensive experiments conducted on various datasets demonstrate that our system outperforms other state-of-the-art visual SLAM systems in illumination-challenging environments. Efficiency evaluations show that our system can run at a rate of 73Hz on a PC and 40Hz on an embedded platform.<br />Comment: 19 pages, 14 figures

Subjects

Subjects :
Computer Science - Robotics

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.03520
Document Type :
Working Paper