Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review
Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however,...
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| Format: | Article |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/810 |
| _version_ | 1818038030141751296 |
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| author | Erzhuo Che Jaehoon Jung Michael J. Olsen |
| author_facet | Erzhuo Che Jaehoon Jung Michael J. Olsen |
| author_sort | Erzhuo Che |
| collection | DOAJ |
| description | Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications. |
| first_indexed | 2024-12-10T07:36:15Z |
| format | Article |
| id | doaj.art-542859a3bfdf429e8a05b8e28a5e9d75 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:36:15Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-542859a3bfdf429e8a05b8e28a5e9d752022-12-22T01:57:25ZengMDPI AGSensors1424-82202019-02-0119481010.3390/s19040810s19040810Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art ReviewErzhuo Che0Jaehoon Jung1Michael J. Olsen2School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USASchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USASchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USAMobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.https://www.mdpi.com/1424-8220/19/4/810point cloudlidarmobile laser scanningfeature extractionsegmentationobject recognitionclassification |
| spellingShingle | Erzhuo Che Jaehoon Jung Michael J. Olsen Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review Sensors point cloud lidar mobile laser scanning feature extraction segmentation object recognition classification |
| title | Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review |
| title_full | Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review |
| title_fullStr | Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review |
| title_full_unstemmed | Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review |
| title_short | Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review |
| title_sort | object recognition segmentation and classification of mobile laser scanning point clouds a state of the art review |
| topic | point cloud lidar mobile laser scanning feature extraction segmentation object recognition classification |
| url | https://www.mdpi.com/1424-8220/19/4/810 |
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