Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm.
作者： Ciyun Lin , Hui Liu , Dayong Wu , Bowen Gong
DOI: 10.3390/S20113054
关键词:
摘要: A light detection and ranging (LiDAR) sensor can obtain richer more detailed traffic flow information than traditional 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 worthless from using a suitable background filtering algorithm accelerate micro-level extraction. This paper presents slice-based projection (SPF) method. First, 3-D projected 2-D polar coordinates reduce dimensions improve processing efficiency. Then, classified into four categories in slice unit: Valuable (VOPs), (WOPs), abnormal ground (AGPs), normal (NGPs). Based on classification results, objects (pedestrians vehicles) their easily identified an individual frame of cloud. We proposed artificial neuron network (ANN)-based model adaptability dealing with gradient LiDAR-employing inclination. The experimental results showed that this successfully extracted points, such as users curbstones. Compared random sample consensus (RANSAC) density-statistic-filtering (3-D-DSF) algorithm, demonstrated better performance terms run-time accuracy.
doaj.org
mdpi.com参考文章(27)
George Vosselman, Sander Dijkman, 3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS ISPRS Workshop: land surface mapping and characterization using laser altimetry. pp. 37- 43 ,(2001)
Arthur E. Hoerl, Robert W. Kennard, Ridge regression: biased estimation for nonorthogonal problems Technometrics. ,vol. 42, pp. 80- 86 ,(2000) , 10.2307/1271436
Sepp Hochreiter, Jürgen Schmidhuber, Long short-term memory Neural Computation. ,vol. 9, pp. 1735- 1780 ,(1997) , 10.1162/NECO.1997.9.8.1735
Qingquan Li, Long Chen, Ming Li, Shih-Lung Shaw, Andreas Nuchter, A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios IEEE Transactions on Vehicular Technology. ,vol. 63, pp. 540- 555 ,(2014) , 10.1109/TVT.2013.2281199
Keqi Zhang, Dean Whitman, Comparison of Three Algorithms for Filtering Airborne Lidar Data Photogrammetric Engineering and Remote Sensing. ,vol. 71, pp. 313- 324 ,(2005) , 10.14358/PERS.71.3.313
Alex J. Smola, Vladimir Vapnik, Linda Kaufman, Christopher J. C. Burges, Harris Drucker, Support Vector Regression Machines neural information processing systems. ,vol. 9, pp. 155- 161 ,(1996)
Ryan Halterman, Michael Bruch, Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection Proceedings of SPIE. ,vol. 7692, ,(2010) , 10.1117/12.850611
Xinhua You, Yuan Zheng, An accurate and practical calibration method for roadside camera using two vanishing points Neurocomputing. ,vol. 204, pp. 222- 230 ,(2016) , 10.1016/J.NEUCOM.2015.09.132
Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, Wolfram Burgard, Motion-based detection and tracking in 3D LiDAR scans international conference on robotics and automation. pp. 4508- 4513 ,(2016) , 10.1109/ICRA.2016.7487649
Alireza Asvadi, Cristiano Premebida, Paulo Peixoto, Urbano Nunes, 3D Lidar-based static and moving obstacle detection in driving environments Robotics and Autonomous Systems. ,vol. 83, pp. 299- 311 ,(2016) , 10.1016/J.ROBOT.2016.06.007