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研究生： 詹學文
Zhan, Xue-Wen
論文名稱： 適用於混沌光達系統高運算吞吐量降噪演算法與架構設計
High-throughput Noise Reduction Algorithm and Architecture Design for Chaotic LiDAR Systems
指導教授： 黃元豪
Huang, Yuan-Hao
口試委員: 黃朝宗
Huang, Chao-Tsung
劉奕汶
Liu, Yi-Wen
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 通訊工程研究所
Communications Engineering
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 69
中文關鍵詞： 混沌光達 、平均分群 、深度圖 、降噪 、光達系統 、硬體架構
外文關鍵詞： Chaotic LiDAR, K-means, Depth map, Noise reduction, LiDAR Systems, Hardware architecture
相關次數： 點閱：4  下載：0 
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    • 光達是一種廣泛應用於遙測的技術。光達透過發射雷射光至目標物並接收反射光，估算出雷射的飛行時間(TOF)進而推算出與目標物之間的距離。光達依據使用不同雷射光源可分成不同的光達系統，其中混沌光達系統具有高精準度、具備抗干擾的能力，因此廣泛的受到討論、研究。然而，因系統是透過計算參考訊號、目標訊號的相關性估算出飛行時間，計算過程繁複導致系統有低的吞吐量。因此，本篇論文提出適用於混沌光達系統的深度圖重建演算法，重建演算法需要較少的運算量並提升吞吐量，系統最大吞吐量約可達到每秒144幀。本篇論文會介紹光達測距系統、TOF估算模組，深度圖重建方法架構設計也會在本篇論文介紹。

    LiDAR is a technology widely used in remote sensing. LiDAR transmits the laser signal to the target and receives the reflected signal, and then estimates the time of flight (TOF) of the laser signal and calculates the distance between the laser and target. According to the different laser sources, the LiDAR can be divided into different LiDAR systems. The chaotic LiDAR system has high precision and anti-interference ability. Therefore, it has been widely discussed and studied. However, because the system estimates the time of flight by calculating the correlation between the reference signal and the target signal, the complicated calculation process leads to low throughput of the system. Therefore, this thesis propose a depth map reconstruction algorithm for chaotic LiDAR systems, which needs less computation and improves throughput. The maximum throughput of the system can achieve about 144 frame/sec. This thesis introduces the entire LiDAR system, TOF estimation module, and the depth map reconstruction architecture design is also presented in this thesis.

    1 Introduction 1 1.1 Chaotic LiDAR 2 1.2 Target Chaotic LiDAR System 4 1.3 Research Motivation 7 1.4 Organization of This Thesis 10 1.5 Notations 10 2 Depth Map Construction of Chaotic LiDAR System 11 2.1 Time of Flight (TOF) Estimation 11 2.1.1 Correlation Between the Reference and Target Signals 12 2.1.2 Spline Interpolation 13 2.2 Depth Map Construction 20 3 Proposed Depth Map Reconstruction Algorithm 23 3.1 Peak to Second Peak Difference (PSPD) Definition 23 3.2 Normalization Method 25 3.3 Two-Stage K-means Clustering 26 3.4 Depth Map Reconstruction 33 3.5 Algorithm Design Flow 35 3.6 Simulation Setup 36 3.7 Simulation Result and Analysis 37 4 Architecture Design 45 4.1 Hardware Architecture 45 4.1.1 Correlation Circuits 48 4.1.2 Peak Finding Circuits 49 4.1.3 Interpolation Circuits 50 4.1.4 Normalization Circuits 51 4.1.5 K-means Clustering Circuits for Stage I 52 4.1.6 Stage II K-means Clustering Circuits for Stage II 54 4.1.7 Reconstruction Circuits 55 4.2 Timing Schedule 56 4.3 Fixed-point Simulation 58 4.4 FPGA Verification 59 4.5 FPGA Synthesis Result 61 5 Conclusion 65 References 67

    [1] F. Pellen, P. Olivard, Y. Guern, J. Cariou, and J. Lotrian, “Radio frequency modulation on an optical carrier for target detection enhancement in sea-water,” Journal of Physics D: Applied Physics, vol. 34, pp. 1122-1130, 2001.
    [2] K. Myneni, T. A. Barr, B. R. Reed, S. D. Pethel, and N. J. Corron, “High-precision ranging using a chaotic laser pulse train,” Applied Physics Letters, vol. 78, no. 11, pp. 1496-1498, 2001.
    [3] F.-Y. Lin and J.-M. Liu, “Chaotic lidar,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 10, no. 5, pp. 991-997, 2004.
    [4] G. N. Pearson, P. J. Roberts, J. R. Eacock, and M. Harris, “Analysis of the performance of a coherent pulsed fiber lidar for aerosol backscatter applications,” Applied Optics, vol. 41, no. 30, pp. 6442-6450, 2002.
    [5] C. Nagasawa, M. Abo, H. Yamamoto, and O. Uchino, “Random modulation cw lidar using new random sequence,” Applied Optics, vol. 29, no. 10, pp. 1466-1470, 1990.
    [6] J. Mark, B. Tromborg, and J. Mark, “Chaos in semiconductor lasers with optical feedback: Theory and experiment,” IEEE Journal of Quantum Electronics, vol. 28, no. 1, pp. 93-108, 1992.
    [7] I. Stancic, M. Brajovic, I. Orovic, and J. Music, “Compressive sensing for reconstruction of 3D point clouds in smart systems,” in 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2016.
    [8] C. Dorea and R. L. de Queiroz, “Depth map reconstruction using color-based region merging,” in 2011 18th IEEE International Conference on Image Processing.IEEE, 2011.
    [9] V. Viacheslav, F. Alexander, M. Vladimir, T. Svetlana, and L. Oksana, “Kinect depth map restoration using modi_ed exemplar-based inpainting,” in IEEE Journal of Quantum Electronics. IEEE, 2014.
    [10] Z. Yan, L. Yu, and Z. Xiong, “Large-area depth recovery for RGB-D camera,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015.
    [11] X. Xiong, D. Chen, W. Zhang, and T. Jia, “Depth map recovery based on optimal scheduling of invalid point filling,” in 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018.
    [12] S.-H. Lee, T.-Y. Chung, J.-Y. Sim, and C.-S. Kim, “E_cient depth map recovery using concurrent object boundaries in texture and depth images,” in 2013 Asia-Paci_c Signal and Information Processing Association Annual Summit and Conference. IEEE, 2013.
    [13] T.-C. Chen, Y.-Y. C. T.-C. Ma, and L.-G. Chen, “Design and implementation of cubic spline interpolation for spike sorting microsystems," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011.
    [14] H. Song, Z. Liu, Y. Xie, L. Wu, and M. Huang, “RGBD co-saliency detection via bagging-based clustering,” IEEE Signal Processing Letters, vol. 23, no. 12, 2016.
    [15] S. Mirkamali and P. Nagabhushan, “RGBD image segmentation,” in 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, 2015.
    [16] J. A. M. H., J. Montero, J. Yanez, and D. Gomez, “A divisive hierarchical k-means based algorihtm for image segmentation,” in 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering. IEEE, 2010.
    [17] T.-S. Xu, H.-D. Chiang, G.-Y. Liu, and C.-W. Tan, “Hierarchical k-means method for clustering large-scale advanced metering infrastructure data,” IEEE Transactions on Power Delivery, vol. 32, no. 2, pp. 609-616, 2015.
    [18] J.-B. Kim, N. Piao, H.-I. Kim, and R.-H. Park, “Depth hole filling for 3-D reconstruction using color and depth images," in The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014). IEEE, 2014.
    [19] M. V. den Bergh, D. Carton, and L. V. Gool, “Depth seeds: Recovering incomplete depth data using superpixels,” in 2013 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2013.
    [20] J.-J. Guo, “Low-complexity depth-sensing algorithm and architecture design for chaotic lidar system,” Master's thesis, National Tsing Hua University, Taiwan, 2019.
    [21] C.-F. Liao, “A 64-QAM 8x8 mimo detector with constant-throughput lattice reduction and QR preprocessing,” Ph.D. dissertation, National Tsing Hua University, Taiwan, 2013.

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