A NOVEL OF COMPARISON BETWEEN 3D HIGH DEFINITION MAPS CREATED BY PHOTOGRAMMETRY AND LASER SCANNING APPLIED FOR AN AUTONOMOUS VEHICLE

Authors

  • Ho Xuan Nang Phenikaa University

DOI:

https://doi.org/10.15625/2525-2518/59/3/15848

Keywords:

Autonomous Vehicle, Point Cloud map, Velodyne, HD map

Abstract

In this paper, based on the selected mathematical algorithm, the performing of two methods for building high-resolution 3D maps that are Photographmetry and Laser scanning was analyzed to find out the advantages and disadvantages of each one. The results showed that the high-resolution map constructed by using lidar was more accurate and detailed, whereas the map constructed by using images with coordinates was more intuitive. A mapping method using lidar-camera fusion was proposed in which the detailed roads are created by Lidar and the rest area built by optical imaging method.

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Author Biography

Ho Xuan Nang, Phenikaa University

Faculty of Vehicle and energy Engineering

References

SAE International Releases Updated Visual Chart for Its “Levels of Driving Automation” Standard for Self-Driving Vehicles, https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-“levels-of-driving-automation”-standard-for-self-driving-vehicles (2018).

Peng H, Ye Q, Shen X. Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks. IEEE Trans Intell Transp Syst. Epub ahead of print 2020. DOI: 10.1109/TITS.2019.2922656.

Brown. Duane C. Brown Memorial Address. Photogramm Eng Remote Sensing.

Chetverikov D, Svirko D, Stepanov D, et al. The trimmed iterative closest point algorithm. In: Proceedings - International Conference on Pattern Recognition. 2002. Epub ahead of print 2002. DOI: 10.1109/icpr.2002.1047997.

Sobreira H, Costa CM, Sousa I, et al. Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform. J Intell Robot Syst Theory Appl 2019; 93: 533–546.

Carballo A, Monrroy A, Wong D, et al. Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform, http://arxiv.org/abs/2004.01374 (2020).

Takeuchi E, Tsubouchi T. A 3-D scan matching using improved 3-D normal distributions transform for mobile robotic mapping. In: IEEE International Conference on Intelligent Robots and Systems. 2006. Epub ahead of print 2006. DOI: 10.1109/IROS.2006.282246.

Akai N, Morales LY, Takeuchi E, et al. Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching. IEEE Intell Veh Symp Proc 2017; 1356–1363.

Xuan Nang H, Anh Son L. DESIGN AND MANUFACTURE THE POINT CLOUD MAP BUILDING SYSTEM FOR AUTONOMOUS VEHICLE BASED ON DIGITAL CAMERA. Vietnam J Mech 2020; 6: 182–187.

Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm. Proc Int Conf 3-D Digit Imaging Model 3DIM 2001; 145–152.

Xuan Nang H, Anh Son L. CREATING HIGH-DEFINITION 3D MAP FOR AUTONOMOUS VEHICLES WITH VELODYNE. J Sci Technol 2020 - Danang University; 18: 44–47.

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Published

09-06-2021

Issue

Section

Mechanical Engineering - Mechatronics