Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching
To meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8905989/ |
| _version_ | 1818665142192177152 |
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| author | Cheng Xin Zhou Jingmei Zhao Xiangmo Wang Hongfei Chang Hui |
| author_facet | Cheng Xin Zhou Jingmei Zhao Xiangmo Wang Hongfei Chang Hui |
| author_sort | Cheng Xin |
| collection | DOAJ |
| description | To meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points. Based on the assumption of flat plane, the improved Lucas-Kanade algorithm is carried out to track feature points, and then the custom LARSAE is used to amend vehicle offsets. Meanwhile, Hu moments are used as the feature descriptor to complete image matching, realizing vehicle motion estimation. These two methods are fused by the discrete kalman filter to update and optimize vehicle position. Experimental results show that the fusion algorithm overcomes the shortcomings of poor positioning accuracy of optical flow and the low processing speed of feature matching, and is able to provide more accurate real-time positioning output, having a certain robustness for circumstances such as illumination change and low pavement texture. |
| first_indexed | 2024-12-17T05:43:56Z |
| format | Article |
| id | doaj.art-b416477cff0f4185ab49b32ec28cef28 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:43:56Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b416477cff0f4185ab49b32ec28cef282022-12-21T22:01:22ZengIEEEIEEE Access2169-35362019-01-01716731016731910.1109/ACCESS.2019.29543418905989Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points MatchingCheng Xin0https://orcid.org/0000-0001-7158-9682Zhou Jingmei1https://orcid.org/0000-0002-0452-4720Zhao Xiangmo2https://orcid.org/0000-0002-0116-5988Wang Hongfei3https://orcid.org/0000-0001-5972-2890Chang Hui4https://orcid.org/0000-0002-0378-8310School of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaTo meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points. Based on the assumption of flat plane, the improved Lucas-Kanade algorithm is carried out to track feature points, and then the custom LARSAE is used to amend vehicle offsets. Meanwhile, Hu moments are used as the feature descriptor to complete image matching, realizing vehicle motion estimation. These two methods are fused by the discrete kalman filter to update and optimize vehicle position. Experimental results show that the fusion algorithm overcomes the shortcomings of poor positioning accuracy of optical flow and the low processing speed of feature matching, and is able to provide more accurate real-time positioning output, having a certain robustness for circumstances such as illumination change and low pavement texture.https://ieeexplore.ieee.org/document/8905989/Vehicle ego-localizationimage matchingoptical flowoptimized FASTKalman filter |
| spellingShingle | Cheng Xin Zhou Jingmei Zhao Xiangmo Wang Hongfei Chang Hui Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching IEEE Access Vehicle ego-localization image matching optical flow optimized FAST Kalman filter |
| title | Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching |
| title_full | Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching |
| title_fullStr | Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching |
| title_full_unstemmed | Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching |
| title_short | Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching |
| title_sort | vehicle ego localization based on the fusion of optical flow and feature points matching |
| topic | Vehicle ego-localization image matching optical flow optimized FAST Kalman filter |
| url | https://ieeexplore.ieee.org/document/8905989/ |
| work_keys_str_mv | AT chengxin vehicleegolocalizationbasedonthefusionofopticalflowandfeaturepointsmatching AT zhoujingmei vehicleegolocalizationbasedonthefusionofopticalflowandfeaturepointsmatching AT zhaoxiangmo vehicleegolocalizationbasedonthefusionofopticalflowandfeaturepointsmatching AT wanghongfei vehicleegolocalizationbasedonthefusionofopticalflowandfeaturepointsmatching AT changhui vehicleegolocalizationbasedonthefusionofopticalflowandfeaturepointsmatching |