FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional sca...

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Bibliographic Details
Published inApplied sciences Vol. 9; no. 1; p. 41
Main Authors Jiang, Guolai, Yin, Lei, Liu, Guodong, Xi, Weina, Ou, Yongsheng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2019
Subjects
Algorithms
Artificial intelligence
Automation
Cameras
Fast Fourier Transform
Fourier transforms
International conferences
Laboratories
laser range finder
Laser range finders
Lasers
Localization
Low cost
Mapping
Methods
Missing data
Mobile robot
Navigation
Optimization
Professional soccer
Registration
Robotics
Robots
Rotation
Sensors
Simultaneous localization and mapping
SLAM
United States > US
China
Macao
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Summary:Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9010041