{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T20:06:42Z","timestamp":1783714002273,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2218-E-035-013"],"award-info":[{"award-number":["MOST 108-2218-E-035-013"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR sensors. The value of the trigonometric functions can be found in seven rotations regardless of the number of input N digits. The number of iterations are reduced by more than half. The experimental results show the similarity value to be all 1 and prove that the LiDAR decoded packet results are exactly the same as the ground truth. The total chip area is 1.93 mm \u00d7 1.93 mm and the core area is 1.32 mm \u00d7 1.32 mm, separately. The number of logic gates is 129,688. The designed chip only takes 0.012 ms and 0.912 ms to decode a packet and a 3D frame of LiDAR sensors, respectively. The throughput of the chip is 8.2105       \u00a0 \u00d7 \u00a0 10   8      bits\/sec. The average power consumption is 237.34 mW at a maximum operating frequency of 100 MHz. This design can not only reduce the number of iterations and the computing time but also reduce the chip area. This paper provides an efficient CORDIC iteration design and solution for LiDAR sensors to reconstruct the point-cloud map for autonomous vehicles.<\/jats:p>","DOI":"10.3390\/s19245412","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T05:54:51Z","timestamp":1575870891000},"page":"5412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Efficient CORDIC Iteration Design of LiDAR Sensors\u2019 Point-Cloud Map Reconstruction Technology"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9599-6415","authenticated-orcid":false,"given":"Yu-Cheng","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Cheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiao-An","family":"Chu","sequence":"additional","affiliation":[{"name":"Sunplus Technology Co., Ltd., Hsinchu 30076, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fan, Y.C., Chu, C.A., and Liu, Y.C. (2019, January 20\u201322). Efficient CORDIC Iteration Design of LiDAR Point Cloud Map Reconstruction Technology. Proceedings of the 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Yilan, Taiwan.","DOI":"10.1109\/ICCE-TW46550.2019.8991704"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dimitrievski, M., Veelaert, P., and Philips, W. (2019). Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle. Sensors, 19.","DOI":"10.3390\/s19020391"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, F., and Knoll, A. (2016). Vehicle Detection Based on Probability Hypothesis Density Filter. Sensors, 16.","DOI":"10.3390\/s16040510"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shahian Jahromi, B., Tulabandhula, T., and Cetin, S. (2019). Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles. Sensors, 19.","DOI":"10.3390\/s19204357"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fan, Y.C., Wu, B.T., Huang, C.J., and Bai, Y.H. (2019, January 11\u201313). Environment Detection of 3D LiDAR by Using Neural Networks. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2019.8662037"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hsiao, S.F., Wen, C.S., and Lee, H.M. (2010, January 18\u201319). Implementation of Floating-point CORDIC Rotation and Vectoring Based on Look up Tables and Multipliers. Proceedings of the International Symposium on Next Generation Electronics (ISNE), Kaohsiung, Taiwan.","DOI":"10.1109\/ISNE.2010.5669143"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shin, M., Kim, J., Jeong, J., and Park, J.B. (2017, January 29\u201331). 3D LiDAR-based Point Cloud Map Registration: Using Spatial Location of Visual Features. Proceedings of the International Conference on Robotics and Automation Engineering (ICRAE), Shanghai, China.","DOI":"10.1109\/ICRAE.2017.8291413"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., and Nunes, U.J. (2017, January 16\u201319). DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet. Proceedings of the International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317880"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TVLSI.2014.2357844","article-title":"VLSI Design of a Depth Map Estimation Circuit Based on Structured Light Algorithm","volume":"23","author":"Fan","year":"2015","journal-title":"IEEE Trans. Very Large Scale Integr. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4987","DOI":"10.1109\/JSTARS.2017.2737362","article-title":"Airborne DInSAR Results Using Time-Domain Backprojection Algorithm: A Case Study over the Slumgullion Landslide in Colorado with Validation Using Spaceborne SAR, Airborne LiDAR, and Ground-Based Observations","volume":"10","author":"Cao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1109\/JSTARS.2016.2628775","article-title":"A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR Data with Optical Images","volume":"10","author":"Kang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6687","DOI":"10.1109\/TGRS.2016.2587798","article-title":"Rapid Updating and Improvement of Airborne LIDAR DEMs through Ground-Based SfM 3-D Modeling of Volcanic Features","volume":"54","author":"Kolzenburg","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Kim, D.Y., Hyeon, J.Y., Shin, D.H., Ju, B.C., Ko, K.N., and Huh, J.C. (2015, January 22\u201325). Measurements and Verification of Ground-based LiDAR in Complex Terrain. Proceedings of the International Conference on Renewable Energy Research and Applications (ICRERA), Palermo, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"26734","DOI":"10.1109\/ACCESS.2017.2771201","article-title":"An Effective Approach for Rock Mass Discontinuity Extraction Based on Terrestrial LiDAR Scanning 3D Point Clouds","volume":"5","author":"Han","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Webster, T. (2017, January 19\u201322). Results from 3 Seasons of Surveys in Maritime Canada Using the Leica Chiroptera II Shallow Water Topobathymetric Lidar Sensor. Proceedings of the International Conference on OCEANS, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084681"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1109\/TLA.2014.6868859","article-title":"Sea Bottom Classification by Means of Bathymetric LIDAR Data","volume":"12","author":"Velasco","year":"2014","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gonsalves, M.O. (2010, January 20\u201323). Using a Dynamic Ocean Surface to Perform a Geometric Calibration of a Bathymetric Lidar. Proceedings of the International Conference on OCEANS, Seattle, WA, USA.","DOI":"10.1109\/OCEANS.2010.5664329"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/JDT.2014.2331064","article-title":"Vivid-DIBR Based 2D to 3D Image Conversion System for 3D Display","volume":"10","author":"Fan","year":"2014","journal-title":"IEEE\/OSA J. Disp. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/TEC.1959.5222693","article-title":"The CORDIC Trigonometric Computing Technique","volume":"EC-8","author":"Volder","year":"1959","journal-title":"IRE Trans. Electron. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/T-C.1974.223800","article-title":"Fourier Transform Computers Using CORDIC Iterations","volume":"C-23","author":"Despain","year":"1974","journal-title":"IEEE Trans. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, J., Li, B., and Zhao, Y. (2016, January 28\u201330). Study of CORDIC Algorithm Based on FPGA. Proceedings of the Chinese Control and Decision Conference (CCDC), Yinchuan, China.","DOI":"10.1109\/CCDC.2016.7531747"},{"key":"ref_22","unstructured":"Nguyen, H.T., Nguyen, X.T., Pham, C.K., Hoang, T.T., and Le, D.H. (2015, January 1\u20134). A Low-resource Low-latency Hybrid Adaptive CORDIC in 180-nm CMOS Technology. Proceedings of the IEEE Region 10 Conference (TENCON), Macao, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T., Nguyen, X.T., Pham, C.K., Hoang, T.T., and Le, D.H. (2016, January 27\u201330). A Parallel Pipeline CORDIC Based on Adaptive Angle Selection. Proceedings of the 2016 International Conference on Electronics, Information, and Communications (ICEIC), Da Nang, Vietnam.","DOI":"10.1109\/ELINFOCOM.2016.7563034"},{"key":"ref_24","unstructured":"Hoang, T.T., Le, D.H., Nguyen, H.T., Nguyen, X.T., and Pham, C.K. (2016, January 22\u201325). A Low-resource Low-Latency Hybrid Adaptive CORDIC with Floating-point Precision. Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montr\u00e9al, QC, Canada."},{"key":"ref_25","unstructured":"Qi, Z., Cabe, A.C., Jones, R.T., and Stan, M.R. (2010, January 18\u201321). CORDIC Implementation with Parameterizable ASIC\/SoC Flow. Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), Concord, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, C.F., and Shiue, M.T. (2014, January 18\u201320). FPGA Prototyping for CORDIC-Based OFDM Baseband Receiver. Proceedings of the Conference on Electron Devices and Solid-State Circuits (EDSSC), Chengdu, China.","DOI":"10.1109\/EDSSC.2014.7061161"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1049\/ip-cds:20050280","article-title":"CORDIC-Based Unified VLSI Architecture for Implementing Window Functions for Real Time Spectral Analysis","volume":"153","author":"Ray","year":"2006","journal-title":"IEE Proc. Circuits Devices Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13980","DOI":"10.3390\/s140813980","article-title":"A Novel Diagnosis Method for a Hall Plates-Based Rotary Encoder with a Magnetic Concentrator","volume":"14","author":"Meng","year":"2014","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13150","DOI":"10.3390\/s121013150","article-title":"A Digitalized Silicon Microgyroscope Based on Embedded FPGA","volume":"12","author":"Xia","year":"2012","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Min, J.H., Kim, S.W., and Swartzlander, E.E. (2011, January 6\u20139). A Floating-Point Fused FFT Butterfly Arithmetic Unit with Merged Multiple-Constant Multipliers. Proceedings of the Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2011.6190055"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:37Z","timestamp":1760190037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,9]]},"references-count":30,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245412"],"URL":"https:\/\/doi.org\/10.3390\/s19245412","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,9]]}}}