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Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA
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Sensors 2019, 19(4), 810; https://doi.org/10.3390/s19040810
Received: 26 December 2018 / Revised: 9 February 2019 / Accepted: 14 February 2019 / Published: 16 February 2019
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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. View Full-Text
Keywords: point cloud; lidar; mobile laser scanning; feature extraction; segmentation; object recognition; classification point cloud; lidar; mobile laser scanning; feature extraction; segmentation; object recognition; classification
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MDPI and ACS Style

Che, E.; Jung, J.; Olsen, M.J. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors 2019, 19, 810. https://doi.org/10.3390/s19040810

AMA Style

Che E, Jung J, Olsen MJ. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors. 2019; 19(4):810. https://doi.org/10.3390/s19040810

Chicago/Turabian Style

Che, Erzhuo; Jung, Jaehoon; Olsen, Michael J. 2019. "Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review" Sensors 19, no. 4: 810. https://doi.org/10.3390/s19040810

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