Abstract
A light detection and ranging (LiDAR) sensor can obtain richer and more detailed traffic flow information than traditional traffic detectors, which could be valuable data input for various novel intelligent transportation applications. However, the point cloud generated by LiDAR scanning not only includes road user points but also other surrounding object points. It is necessary to remove the worthless points from the point cloud by using a suitable background filtering algorithm to accelerate the micro-level traffic data extraction. This paper presents a background point filtering algorithm using a slice-based projection filtering (SPF) method. First, a 3-D point cloud is projected to 2-D polar coordinates to reduce the point data dimensions and improve the processing efficiency. Then, the point cloud is classified into four categories in a slice unit: Valuable object points (VOPs), worthless object points (WOPs), abnormal ground points (AGPs), and normal ground points (NGPs). Based on the point cloud classification results, the traffic objects (pedestrians and vehicles) and their surrounding information can be easily identified from an individual frame of the point cloud. We proposed an artificial neuron network (ANN)-based model to improve the adaptability of the algorithm in dealing with the road gradient and LiDAR-employing inclination. The experimental results showed that the algorithm of this paper successfully extracted the valuable points, such as road users and curbstones. Compared to the random sample consensus (RANSAC) algorithm and 3-D density-statistic-filtering (3-D-DSF) algorithm, the proposed algorithm in this paper demonstrated better performance in terms of the run-time and background filtering accuracy.
Highlights
As a type of active vision sensor, light detection and ranging (LiDAR) is a 3-D point cloud imaging vision sensor with the advantages of insensitivity to external light changes, strong adaptability to complex environments, wide coverage, and is informative
The algorithm proposed in this paper aims to remove worthless object points (WOPs) and normal ground points (NGPs) from the point as much as possible
We developed a novel method of background point filtering for low-channel infrastructure-based LiDAR
Summary
As a type of active vision sensor, light detection and ranging (LiDAR) is a 3-D point cloud imaging vision sensor with the advantages of insensitivity to external light changes, strong adaptability to complex environments, wide coverage, and is informative. The most typical application of LiDAR in intelligent transportation systems is to detect road and traffic information for automatic driving vehicles [1,2,3,4]. Tesla and Uber have experienced fatal accidents during their self-driving tests [5,6]. It is very important for Sensors 2020, 20, 3054; doi:10.3390/s20113054 www.mdpi.com/journal/sensors. A light detection and ranging (LiDAR) sensor can obtain richer and more detailed traffic flow information than traditional traffic detectors, which could be valuable data input for various novel intelligent transportation applications.
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