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.
Uncrewed aircraft systems (UASs) with integrated light detection and ranging (lidar) technology are becoming an increasingly popular and efficient remote sensing method for mapping. Due to its quick deployment and comparatively inexpensive cost, uncrewed laser scanning (ULS) can be a desirable solution to conduct topographic surveys for areas sized on the order of square kilometers compared to the more prevalent and mature method of airborne laser scanning (ALS) used to map larger areas. This paper rigorously assesses the accuracy and quality of a ULS system with comparisons to terrestrial laser scanning (TLS) data, total station (TS) measurements, and Global Navigation Satellite System (GNSS) check points. Both the TLS and TS technologies are ideal for this assessment due to their high accuracy and precision. Data for this analysis were collected over a period of two days to map a landslide complex in Mulino, Oregon. Results show that the digital elevation model (DEM) produced from the ULS had overall vertical accuracies of approximately 6 and 13 cm at 95% confidence when compared to the TS cross-sections for the road surface only and road and vegetated surfaces, respectively. When compared to the TLS data, overall biases of −2.4, 1.1, and −2.7 cm were observed in X, Y, and Z with a 3D RMS difference of 8.8 cm. Additional qualitative and quantitative assessments discussed in this paper show that ULS can provide highly accurate topographic data, which can be used for a wide variety of applications. However, further research could improve the overall accuracy and efficiency of the cloud-to-cloud swath adjustment and calibration processes for georeferencing the ULS point cloud.
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