Abstract
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.
Highlights
With the rapid development of artificial intelligence and pattern recognition technology, intelligent robots have entered all aspects of industrial automation and human life
To address the low detection range problem of low-cost laser range finder (LRF) sensors, we added a pre-alignment module based on missing data features to determine the rough rotation angle before Fast Fourier Transform (FFT) processing under certain conditions
We propose a new solution for FFT-Iterative Closest Point (ICP) scan-matching with low-cost LRF sensors
Summary
With the rapid development of artificial intelligence and pattern recognition technology, intelligent robots have entered all aspects of industrial automation and human life. Scan matching may often fail while using low-cost LRF sensors, especially when the robot is running too quickly in complex environments, and it may cause failure to mapping and localization. To address the low detection range problem of low-cost LRF sensors, we added a pre-alignment module based on missing data features to determine the rough rotation angle before FFT processing under certain conditions. The rest of the paper is organized as follows: In Section 2, we propose robot modeling and data pretreatment, as well as missing data feature extracting and matching; In Section 3, the FFT-based scan-matching algorithm is introduced; In Section 4, the complete solution of the improved FFT-ICP scan-matching is proposed; In Section 5, by using a cleaning robot with only a low-cost LRF sensor in different complex indoor environments, experiments are completed with the proposed method and comparative methods.
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