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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| Veröffentlicht in: | Electronics (Basel) Jg. 11; H. 11; S. 1759 |
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01.06.2022
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| Abstract | 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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| AbstractList | 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. |
| Author | Qian, Fufu Sui, Lianjie Yu, Hanqi Liu, Yiting Qian, Kui Lu, Yuhao Zhang, Lei Yan, Tingwu Gao, Fangzheng |
| Author_xml | – sequence: 1 givenname: Yiting surname: Liu fullname: Liu, Yiting – sequence: 2 givenname: Lei surname: Zhang fullname: Zhang, Lei – sequence: 3 givenname: Kui surname: Qian fullname: Qian, Kui – sequence: 4 givenname: Lianjie surname: Sui fullname: Sui, Lianjie – sequence: 5 givenname: Yuhao surname: Lu fullname: Lu, Yuhao – sequence: 6 givenname: Fufu surname: Qian fullname: Qian, Fufu – sequence: 7 givenname: Tingwu surname: Yan fullname: Yan, Tingwu – sequence: 8 givenname: Hanqi surname: Yu fullname: Yu, Hanqi – sequence: 9 givenname: Fangzheng surname: Gao fullname: Gao, Fangzheng |
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| Cites_doi | 10.1109/ACCESS.2020.3016424 10.1023/B:JINT.0000038945.55712.65 10.1109/ACCESS.2015.2455871 10.1109/TIP.2014.2387020 10.1051/itmconf/20171105001 10.1016/j.patcog.2020.107206 10.1088/1742-6596/2095/1/012053 10.1117/12.2580630 10.9746/jcmsi.13.138 10.12720/joace.2.3.270-276 10.1109/LRA.2021.3068682 10.1109/ICARCV.2010.5707254 10.1007/s12524-021-01358-x |
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| SubjectTerms | Accuracy Algorithms Autonomous navigation Environmental testing Feature extraction Laser applications Lasers Lidar Noise Radar Radar scanning Segmentation Segments Sensors Unknown environments |
| Title | An Adaptive Threshold Line Segment Feature Extraction Algorithm for Laser Radar Scanning Environments |
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