3D local feature BKD to extract road information from mobile laser scanning point clouds
Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Bisheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid - Skiadopoulos, V. ELSEVIER, 2013, official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:130 ; year:2017 ; pages:329-343 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.isprsjprs.2017.06.007 |
|---|
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ELV030571715 |
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| 245 | 1 | 0 | |a 3D local feature BKD to extract road information from mobile laser scanning point clouds |
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| 520 | |a Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. | ||
| 520 | |a Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. | ||
| 650 | 7 | |a Curb |2 Elsevier | |
| 650 | 7 | |a Feature extraction |2 Elsevier | |
| 650 | 7 | |a Point cloud processing |2 Elsevier | |
| 650 | 7 | |a Detection |2 Elsevier | |
| 650 | 7 | |a Road marking |2 Elsevier | |
| 700 | 1 | |a Liu, Yuan |4 oth | |
| 700 | 1 | |a Dong, Zhen |4 oth | |
| 700 | 1 | |a Liang, Fuxun |4 oth | |
| 700 | 1 | |a Li, Bijun |4 oth | |
| 700 | 1 | |a Peng, Xiangyang |4 oth | |
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10.1016/j.isprsjprs.2017.06.007 doi GBV00000000000356.pica (DE-627)ELV030571715 (ELSEVIER)S0924-2716(17)30017-5 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Yang, Bisheng verfasserin aut 3D local feature BKD to extract road information from mobile laser scanning point clouds 2017transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Curb Elsevier Feature extraction Elsevier Point cloud processing Elsevier Detection Elsevier Road marking Elsevier Liu, Yuan oth Dong, Zhen oth Liang, Fuxun oth Li, Bijun oth Peng, Xiangyang oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:130 year:2017 pages:329-343 extent:15 https://doi.org/10.1016/j.isprsjprs.2017.06.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 130 2017 329-343 15 |
| spelling |
10.1016/j.isprsjprs.2017.06.007 doi GBV00000000000356.pica (DE-627)ELV030571715 (ELSEVIER)S0924-2716(17)30017-5 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Yang, Bisheng verfasserin aut 3D local feature BKD to extract road information from mobile laser scanning point clouds 2017transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Curb Elsevier Feature extraction Elsevier Point cloud processing Elsevier Detection Elsevier Road marking Elsevier Liu, Yuan oth Dong, Zhen oth Liang, Fuxun oth Li, Bijun oth Peng, Xiangyang oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:130 year:2017 pages:329-343 extent:15 https://doi.org/10.1016/j.isprsjprs.2017.06.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 130 2017 329-343 15 |
| allfields_unstemmed |
10.1016/j.isprsjprs.2017.06.007 doi GBV00000000000356.pica (DE-627)ELV030571715 (ELSEVIER)S0924-2716(17)30017-5 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Yang, Bisheng verfasserin aut 3D local feature BKD to extract road information from mobile laser scanning point clouds 2017transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Curb Elsevier Feature extraction Elsevier Point cloud processing Elsevier Detection Elsevier Road marking Elsevier Liu, Yuan oth Dong, Zhen oth Liang, Fuxun oth Li, Bijun oth Peng, Xiangyang oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:130 year:2017 pages:329-343 extent:15 https://doi.org/10.1016/j.isprsjprs.2017.06.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 130 2017 329-343 15 |
| allfieldsGer |
10.1016/j.isprsjprs.2017.06.007 doi GBV00000000000356.pica (DE-627)ELV030571715 (ELSEVIER)S0924-2716(17)30017-5 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Yang, Bisheng verfasserin aut 3D local feature BKD to extract road information from mobile laser scanning point clouds 2017transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Curb Elsevier Feature extraction Elsevier Point cloud processing Elsevier Detection Elsevier Road marking Elsevier Liu, Yuan oth Dong, Zhen oth Liang, Fuxun oth Li, Bijun oth Peng, Xiangyang oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:130 year:2017 pages:329-343 extent:15 https://doi.org/10.1016/j.isprsjprs.2017.06.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 130 2017 329-343 15 |
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10.1016/j.isprsjprs.2017.06.007 doi GBV00000000000356.pica (DE-627)ELV030571715 (ELSEVIER)S0924-2716(17)30017-5 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Yang, Bisheng verfasserin aut 3D local feature BKD to extract road information from mobile laser scanning point clouds 2017transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Curb Elsevier Feature extraction Elsevier Point cloud processing Elsevier Detection Elsevier Road marking Elsevier Liu, Yuan oth Dong, Zhen oth Liang, Fuxun oth Li, Bijun oth Peng, Xiangyang oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:130 year:2017 pages:329-343 extent:15 https://doi.org/10.1016/j.isprsjprs.2017.06.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 130 2017 329-343 15 |
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Enthalten in In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid Amsterdam [u.a.] volume:130 year:2017 pages:329-343 extent:15 |
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Enthalten in In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid Amsterdam [u.a.] volume:130 year:2017 pages:329-343 extent:15 |
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3D local feature BKD to extract road information from mobile laser scanning point clouds |
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Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. |
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Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. |
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Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. |
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