Abstract
The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.
Highlights
The advancement in mobile laser scanning (MLS) technology, integrated with laser scanners, location and navigation sensors (e.g., Global Navigation Satellite Systems (GNSS), and Inertial Measurement Unit (IMU)), and imagery data acquisition sensors on moving platforms has enhanced the performance of MLS in static urban objects detection, extraction, and modeling [1]
Digital cameras play a secondary role that they are applied in visualization, while laser scanners that are integrated into MLS systems are the primary source of precisely georeferenced data [14]
With the assumptions that road surfaces are large planes with a certain distance to trajectory data of MLS systems and the normal vectors of road surfaces are approximately parallel to the Z-axis, Yang et al [53] segmented road surfaces by generating geo-referenced feature (GRF) images to filter out off-ground objects from the raw MLS point clouds
Summary
The advancement in mobile laser scanning (MLS) technology, integrated with laser scanners, location and navigation sensors (e.g., Global Navigation Satellite Systems (GNSS), and Inertial Measurement Unit (IMU)), and imagery data acquisition sensors (e.g., panoramic and digital cameras) on moving platforms has enhanced the performance of MLS in static urban objects detection, extraction, and modeling [1] These tasks using various geospatial point cloud data with different geometric attributes, complex structure, and variable intensities, have become one of the popular topics in disciplines, such as photogrammetry, remote sensing, and computer vision [1]. We review the state-of-the-art traditional and learning-based road object detection and extraction methods and discuss their performance and applicability. Challenges and future research are discussed and a conclusion is presented
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