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

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