Abstract
Nowadays, LiDAR (Light Detection and Ranging) is used in many fields, such as transportation. Thanks to the recent technological improvements, the current generation of LiDAR mapping instruments available on the market allows to acquire up to millions of three-dimensional (3D) points per second. On the one hand, such improvements allowed the development of LiDAR-based systems with increased productivity, enabling the quick acquisition of detailed 3D descriptions of the objects of interest. However, on the other hand, the extraction of the information of interest from such huge amount of acquired data can be quite challenging and time demanding. Motivated by such observation, this paper proposes the use of the Optimum Dataset method in order to ease and speed up the information extraction phase by significantly reducing the size of the acquired dataset while preserving (retain) the information of interest. This paper focuses on the data reduction of LiDAR datasets acquired on roads, with the goal of extraction the off-road objects. Mostly motivated by the need of mapping roads and quickly determining car position along a road, the development of efficient methods for the extraction of such kind of information is becoming a hot topic in the research community.
Highlights
Thanks to the high accuracy and reliability of Light Detection and Ranging (LiDAR) scanners, the use of such instruments has become the state-of-the-art of static and mobile mapping systems during the past decade
Since the direct application of object detection methods on large raw LiDAR datasets can be computationally inefficient, this paper considered the use of the Optimum Dataset (OptD) method as a pre-processing step in order to tremendously reduce the dataset size, while keeping most of the geometric information of interest for the considered application
The OptD method was applied on MLS and ALS datasets assessing the suitability of its data reduction for what concerns the detection of three classes of off-road objects: light poles, traffic signs, and power lines
Summary
Thanks to the high accuracy and reliability of Light Detection and Ranging (LiDAR) scanners, the use of such instruments has become the state-of-the-art of static and mobile mapping systems during the past decade. Given the possibility of using LiDAR sensors in both terrestrial and aerial surveys (Terrestrial Laser Scanning (TLS), Mobile Laser Scanning (MLS), Airborne Laser Scanning (ALS)), the number of applications exploiting such kind of technology is continuously increasing, including nowadays several fields such as civil and structural engineering [1], forestry and environmental protection [2], road engineering [3], and assisted/autonomous driving. Since due to occlusions some objects are not properly mapped by a single LiDAR mobile scanner, nowadays MLS systems are often provided with several. The use of such multiple LiDAR systems ensures the collection of a spatially more complete dataset, on the other hand, the size of the collected point cloud quickly becomes huge
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