Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm
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 in...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/11/3054 |
| _version_ | 1797566830171127808 |
|---|---|
| author | Ciyun Lin Hui Liu Dayong Wu Bowen Gong |
| author_facet | Ciyun Lin Hui Liu Dayong Wu Bowen Gong |
| author_sort | Ciyun Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:32:56Z |
| format | Article |
| id | doaj.art-ab276b69e8814f3ea2c9a9cb435432bc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:32:56Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab276b69e8814f3ea2c9a9cb435432bc2023-11-20T02:00:46ZengMDPI AGSensors1424-82202020-05-012011305410.3390/s20113054Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering AlgorithmCiyun Lin0Hui Liu1Dayong Wu2Bowen Gong3Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaDepartment of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaTexas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USADepartment of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaA 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.https://www.mdpi.com/1424-8220/20/11/3054background points filteringinfrastructure-based LiDARslice-based projection3-D point cloud |
| spellingShingle | Ciyun Lin Hui Liu Dayong Wu Bowen Gong Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm Sensors background points filtering infrastructure-based LiDAR slice-based projection 3-D point cloud |
| title | Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm |
| title_full | Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm |
| title_fullStr | Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm |
| title_full_unstemmed | Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm |
| title_short | Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm |
| title_sort | background point filtering of low channel infrastructure based lidar data using a slice based projection filtering algorithm |
| topic | background points filtering infrastructure-based LiDAR slice-based projection 3-D point cloud |
| url | https://www.mdpi.com/1424-8220/20/11/3054 |
| work_keys_str_mv | AT ciyunlin backgroundpointfilteringoflowchannelinfrastructurebasedlidardatausingaslicebasedprojectionfilteringalgorithm AT huiliu backgroundpointfilteringoflowchannelinfrastructurebasedlidardatausingaslicebasedprojectionfilteringalgorithm AT dayongwu backgroundpointfilteringoflowchannelinfrastructurebasedlidardatausingaslicebasedprojectionfilteringalgorithm AT bowengong backgroundpointfilteringoflowchannelinfrastructurebasedlidardatausingaslicebasedprojectionfilteringalgorithm |