Algorithm of Noise Remover Development on LIDAR’s Point Cloud Data 2D for autonomous electrical vehicle application

  • Mas’ud Abdur Rosyid Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
  • Yusuf Suhaimi Daulay Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
  • Denden Mohamad Arifin Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
  • Ardian Infantono Prodi Teknik Aeronautika Pertahanan, Akademi Angkatan Udara, Yogyakarta, Indonesia
  • Arief Suryadi Satyawan Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
  • Ema Ema Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
  • Raden Aditya Satria Nugraha Universitas Nurtanio Bandung /Badan Riset Dan Inovasi Nasional
Keywords: Autonomous electric vehicles, LiDAR 2D, Noise removal

Abstract

The application of 2-dimensional LiDAR (Light Detection And Ranging) technology is sometimes constrained by the presence of data anomalies or noise that affects the accuracy in detecting real objects. If it is not handled properly, it can interfere with its work operations, especially if it is applied to autonomous electric vehicles. Therefore, efforts are needed to reduce noise which is implemented in LiDAR data processing software. In this study, the development of noise reduction technology that appears in the two-dimensional LiDAR data point cloud is carried out. The concept applied is the development of a systematic LiDAR data processing algorithm. The design of this algorithm contains visualization of object detection, storage of LiDAR data point cloud as detected object information, as well as noise reduction methods on the two-dimensional LiDAR data point cloud. This algorithm is realized in software form on Raspberry Pi 4 hardware, using the Python programming language. There are six algorithms used to reduce or eliminate noise, namely Algorithm 1, Algorithm 2, Algorithm 3, Algorithm 4, Algorithm 5, Algorithm 6. The experimental results show that the six algorithms created are able to display data visualization based on a 2-dimensional mapping system that is corrected for noise. The six algorithms succeeded in selecting noise up to 100%, although approximately 80% of the data that were considered correct could not be presented. Even if only 20% of the data is correct, the object structure is still recognizable

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References

Data Sheet YDLIDAR G4, https://www.ydlidar.com/Public/upload/files/2020-10-29/YDLIDAR%20G4%20Datasheet.pdf, Diunduh pada tanggal 30 November 2020 Pukul 10.00 WIB

Raspberry Pi 4, https://www.ros.org/about-ros/, Diakses pada tanggal 10 Januari 2021 Pukul 16.00 WIB

Bahasa Pemrograman Python https://www.pythonindo.com/menggunakan-idle-python/, Diakses pada tanggal 07 Desember 2020 Pukul 13.00 WIB

Prayoga,S.,Budianto,S.,Kusuma Atmaja,AB.,system pemetaan ruangan 2D menggunakan Lidar, Vol 9 No. 1 April 2017, 73-79, April 2017.

Catapang, AN., Manuel Ramos,jr., obstacle detection using a 2D LIDAR system for an autonomous vehicle, 2016 6th internasional conference on control system, computing and engineering, 25-27 November 2016, Penang, Malaysia.

Cheng,D., Zhao,D., Wei,C., Tian,D., PCA-Based Denoising Algorithm For outdor Lidar Point Cloud Data. Sensors 2021, https://doi.org/10.3390/s21113703 , 26 may 2021

Lakshmanmallidi. 2019. PyLiDAR3. https://github.com/lakshmanmallidi/Py Lidar3 , diakses pada tanggal 18 April 2020.

Ravankar,AA., Kobayashi,Y., Jixin,LV., Emaru,T., Hoshino,Y., an embarrassingly parallel hopping window noise removing algorithm for lidar based robot mapping, SICE Annual conference 2014, September 9-14, 2014, Hokkaido university, Sappora, Japan.

Richard Blum, python programming for raspberry pi in 24 hours,sams teach yourself, 2014.

Zuowei,H., Yuanjiang,H., Jie,H., a method for noise removal of LIDAR point cloud, 2013 third internasional conference on intelegenct system design and engineering application, Doi 10.1109/ISDDEA.2012.32.

Flavio B.P. Malavazi, Remy Guyonneau, Jean-Baptiste Fasquel, Sebastien Lagrange dan Franck Mercier LiDAR-only based navigation algorithm for an autonomous agricultural robot 2018

Angelo Nikko Catapang dan Manuel Ramos, Jr Obstacle detection using a 2D LiDAR System for an Autonomous vehicle 2016.

Ankit A. Ravankar, Yukinori Kobayashi, Jixin Lv, Takanori Emaru dan Yohei Hoshino An embarrassingly parallel hopping window noise removing algorithm for lidar based robot mapping 2014./

Satyawan, A. S., Kurniawan, D., Armi, N., dan Wijayanto, Y. N., Room Map Estimation from Two-Dimensional Lidar's Point Cloud Data, 2019 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), oktober, 2019, Tangerang, Indonesia, 2019, pp. 152-155.
Published
2021-12-21
How to Cite
Rosyid, M., Daulay, Y., Arifin, D., Infantono, A., Satyawan, A., Ema, E., & Nugraha, R. (2021). Algorithm of Noise Remover Development on LIDAR’s Point Cloud Data 2D for autonomous electrical vehicle application. Prosiding Seminar Nasional Sains Teknologi Dan Inovasi Indonesia (SENASTINDO), 3, 145 - 156. https://doi.org/10.54706/senastindo.v3.2021.146

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