An Adaptive Threshold Line Segment Feature Extraction Algorithm for Laser Radar Scanning Environments

An accurate map is needed for the autonomous navigation of mobile robots in unknown environments. The application of laser radars has the advantages of high ranging accuracy and long ranging distances. Due to the small amount of data on laser radars and the influence of noise on the sensor itself, t...

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Vydáno v:Electronics (Basel) Ročník 11; číslo 11; s. 1759
Hlavní autoři: Liu, Yiting, Zhang, Lei, Qian, Kui, Sui, Lianjie, Lu, Yuhao, Qian, Fufu, Yan, Tingwu, Yu, Hanqi, Gao, Fangzheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2022
Témata:
Accuracy
Algorithms
Autonomous navigation
Environmental testing
Feature extraction
Laser applications
Lasers
Lidar
Noise
Radar
Radar scanning
Segmentation
Segments
Sensors
Unknown environments
ISSN:2079-9292, 2079-9292
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Shrnutí:An accurate map is needed for the autonomous navigation of mobile robots in unknown environments. The application of laser radars has the advantages of high ranging accuracy and long ranging distances. Due to the small amount of data on laser radars and the influence of noise on the sensor itself, these amount to causing problems such as low accuracies of map construction and large positioning errors. Currently, the feature extraction of environmental line segments based on radar scanning data generally adopts the idea of recursion. However, the amount of calculations for applying recursion is large, and the threshold of extracted feature points needs to be set manually. Moreover, the fixed segmentation threshold will cause under-segmentation or over-segmentation. In this paper, an adaptive threshold-based feature extraction method for environmental line segments is proposed. The method denoises the original data first, and then an adaptive threshold of the nearest neighbor algorithm is provided to improve the accuracy of breakpoint judgment; next, the slope difference between adjacent line segments is evaluated according to the line segment fitting error in order to obtain the optimal corner feature. Finally, the point set is segmented to fit line-segment features. Based on actual environment tests, the environmental similarity of the line segment features extracted by the new algorithm in this paper increases by 8.3% compared with the IEPF (Iterative End Point Fit) algorithm. The algorithm avoids recursive operations, improves the efficiency by four times, and meets the real-time requirements of line segment fitting.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11111759