110 research outputs found

    Data-guided statistical sparse measurements modeling for compressive sensing

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    Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples

    Optimization of ROI-based LiDAR sampling in on-road environment for autonomous driving

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. Hyuk-Jae Lee.Light detection and ranging (LiDAR) ์„ผ์„œ๋Š” ์ตœ๊ทผ ๋กœ๋ณดํ‹ฑ์Šค์™€ ์ž์œจ ์ฃผํ–‰์„ ๋น„๋กฏํ•œ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฐ LiDAR ์„ผ์„œ๋Š” ๋‹ค๋ฅธ ์„ผ์„œ๋ณด๋‹ค ๋‚ฎ์€ ํ•ด์ƒ๋„๊ฐ€ ํŠน์ง•์œผ๋กœ, ํšจ๊ณผ์ ์ธ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ค๊ณ„ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ž์œจ ์ฃผํ–‰์— ์ ์šฉ๋˜๋Š” LiDAR ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฒฝ์šฐ ๋„๋กœ์˜ ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ๋„ ๊ฐ•์ธํ•˜๊ฒŒ ๋†’์€ ํ’ˆ์งˆ๋กœ reconstruction์„ ํ•˜๋Š” ๊ฒƒ์ด ๋ชฉํ‘œ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ˜„ํ–‰ ROI ๊ธฐ๋ฐ˜ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์‹œ๋ฉ˜ํ‹ฑ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ๊ฐ์ฒด, ๋„๋กœ, ๋ฐฐ๊ฒฝ ๋“ฑ์— ๋”ฐ๋ฅธ sampling rate๋Š” ์ง€๊ธˆ๊นŒ์ง€ ์ถฉ๋ถ„ํžˆ ๋…ผ์˜๋˜์ง€ ์•Š์•˜๊ณ , ์ด๋กœ ์ธํ•ด ์ข…ํ•ฉ์ ์ธ reconstruction ํ’ˆ์งˆ์ด ์ €ํ•˜๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ์ฒด, ๋„๋กœ, ๋ฐฐ๊ฒฝ์— ๋”ฐ๋ฅธ sampling budget ratio๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ฐ์ฒด, ๋„๋กœ, ๋ฐฐ๊ฒฝ์˜ ํŠน์„ฑ์ด ์ƒ˜ํ”Œ๋ง ์ด์ „์— ์„ ํ–‰ ์ง€์‹์œผ๋กœ ์ฃผ์–ด์ ธ ์žˆ๋‹ค๋Š” ๊ฐ€์ •์„ ์ด์šฉํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” sampling budget์„ ์ ์šฉํ•œ ๊ฒฐ๊ณผ, ํ˜„ํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋ณด๋‹ค ๊ฐ์ฒด์— ๋Œ€ํ•œ mean-absolute-error (MAE)๋Š” ์ตœ๋Œ€ 45.92% ๊ฐ์†Œํ•˜์˜€์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ „๋ฐ˜์ ์ธ MAE ๋˜ํ•œ 3.36% ๊ฐ์†Œํ•˜์˜€๊ณ , ๋„๋กœ์— ๋Œ€ํ•œ MAE๋Š” ์˜ค์ง 54.18% ๊ฐ์†Œํ•˜์˜€๋‹ค.In recent years, light detection and ranging (LiDAR) sensors have been applied in several situations, including robotics and autonomous driving. However, LiDAR sensors have relatively low resolutions. Therefore, it is imperative to design an effective sampling algorithm for LiDAR sensors. To manage complex on-road environments, conventional ROI-based LiDAR sampling algorithm utilizes semantic information to achieve robust and high reconstruction quality. However, the ratio between sampling rates of objects, roads, and background areas is not thoroughly investigated. Therefore, the overall reconstruction quality may be degraded. To address this problem, this study presents a proposed method to examine the sampling budget ratio between objects, roads, and background areas, under the assumption that characteristics of objects, roads, and background areas are known prior to sampling. Experimental results depict a significant reduction in the mean-absolute-error (MAE) of the object region, road region and overall region by up to 45.92%, 54.18% and 3.36% under the proposed method, respectively, compared to the conventional method.Chapter 1. Introduction ๏ผ‘ 1.1. Overview ๏ผ‘ 1.2. Light detection and ranging sensor LiDAR sampling ๏ผ‘ Chapter 2. Background ๏ผ” 2.1. Definition of a sampling problem ๏ผ” 2.2. Oracle Random Sampling ๏ผ” 2.2.1 Sampling Model ๏ผ” 2.2.2 Oracle Random Scheme ๏ผ• 2.3. ROI-based LiDAR sampling algorithm ๏ผ– Chapter 3. Proposed method ๏ผ˜ 3.1. Analytical method ๏ผ˜ Chapter 4. Experimental results ๏ผ‘๏ผ• 4.1. Dataset ๏ผ‘๏ผ• 4.2 Quantitative evaluation ๏ผ‘๏ผ– Chapter 5. Conclusion ๏ผ’๏ผ Appendix ๏ผ’๏ผ‘ References ๏ผ“๏ผ‘ ์ดˆ ๋ก(Abstract in Korean) ๏ผ“๏ผ’Maste

    ๋“€์–ผ๋ฏธ๋Ÿฌ ๋ผ์ด๋‹ค ์ด๋ฏธ์ง•์„ ์œ„ํ•œ ํƒ€์ด๋ฐ์ด ๊ณ ๋ ค๋œ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. Lee, Hyuk-Jae.In recent years, active sensor technologies such as light detection and ranging (LIDAR) have been intensively studied in theory and widely adopted in many applications, i.e., self-driving cars, robotics and sensing. Generally, the spatial resolution of a depth-acquisition device, such as a LiDAR sensor, is limited because of a slow acquisition speed. To accurately reconstruct a depth image from a limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires a large amount of computation for reconstruction. A mathematical formulation of a LiDAR system demonstrates that the existing two-stage sampling does not satisfy its timing constraint for practical use. Therefore, designing a LiDAR system with an efficient sampling algorithm is a significant technological challenge. Firstly, this thesis addresses the problem of adopting the state-of-art laser marking system of a dual-mirror deflection scanner when creating a high-definition LIDAR system. Galvanometer scanners are modeled and parameterized based on concepts of their controllers and the well-known raster scanning method. The scanning strategy is then modeled and analyzed considering the physical scanning movement and the minimum spanning tree. From this analysis, the link between the quality of the captured image of a field of view (FOV) and the scanning speed is revealed. Furthermore, sufficient conditions are derived to indicate that the acquired image fully covers the FOV and that the captured objects are well aligned under a specific frame rate. Finally, a sample LIDAR system is developed to illustrate the proposed concepts. Secondly, to overcome the drawbacks of two-stage sampling, we propose a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern is derived from a k-NN expanding operation from the downsampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. Experimental results with Middlebury datasets and Brown laser-range datasets are presented. Thirdly, state-of-the-art adaptive methods such as two-step sampling are highly effective while addressing indoor, less complex scenes at a moderately low sampling rate. However, their performance is relatively low in complex on-road environments, particularly when the sampling rate of the measuring equipment is low. To address this problem, this thesis proposes a region-of-interest-(ROI)-based sampling algorithm in on-road environments for autonomous driving. With the aid of fast and accurate road and object detection algorithms, particularly those based on convolutional neural networks (CNNs), the proposed sampling algorithm utilizes the semantic information and effectively distributes samples in road, object, and background areas. Experimental results with KITTI datasets are presented.์ตœ๊ทผ LIDAR (light detection and ranging)์™€ ๊ฐ™์€ ๋Šฅ๋™์  ์„ผ์„œ ๊ธฐ์ˆ ์€ ์ด๋ก ์ ์œผ๋กœ๋„ ์ง‘์ค‘์ ์œผ๋กœ ์—ฐ๊ตฌ๋˜์—ˆ๊ณ , ์ž์œจ์ฃผํ–‰์ฐจ, ๋กœ๋ด‡, ์„ผ์‹ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ LiDAR ์„ผ์„œ์™€ ๊ฐ™์€ ์‹ฌ๋„์ธก์ •์žฅ์น˜๋Š” ๋Š๋ฆฐ ์†๋„ ๋•Œ๋ฌธ์— ๊ณต๊ฐ„์  ํ•ด์ƒ๋„๊ฐ€ ์ œํ•œ๋œ๋‹ค. ์ œํ•œ๋œ ๊ณต๊ฐ„์  ํ•ด์ƒ๋„๋กœ๋ถ€ํ„ฐ ์‹ฌ๋„ ์ด๋ฏธ์ง€๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์žฌ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ 2๋‹จ๊ณ„ ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์ด ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 2๋‹จ๊ณ„ ์ƒ˜ํ”Œ๋ง์€ ๋ถˆ๊ทœ์น™์ ์ธ ์ƒ˜ํ”Œ๋ง ํŒจํ„ด์œผ๋กœ ์ƒ˜ํ”Œ๋ง์„ ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์žฌ๊ตฌ์„ฑ ๊ณผ์ •์— ๋งŽ์€ ์–‘์˜ ์—ฐ์‚ฐ์ด ํ•„์š”ํ•˜๋‹ค. LiDAR ์‹œ์Šคํ…œ์„ ์ˆ˜ํ•™์ ์ธ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์„ ๋•Œ, ๊ธฐ์กด์˜ 2๋‹จ๊ณ„ ์ƒ˜ํ”Œ๋ง์€ ์‹ค์šฉ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ธฐ ์œ„ํ•œ ํƒ€์ด๋ฐ ์ œ์•ฝ ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜์ง€ ๋ชปํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํšจ์œจ์ ์ธ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜๋Š” LiDAR ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ๊ธฐ์ˆ ์  ๊ณผ์ œ์ด๋‹ค. ์ฒซ์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์ตœ์‹ ์˜ ๋ ˆ์ด์ € ๋งˆํ‚น ์‹œ์Šคํ…œ์„ dual-mirror ์Šค์บ๋„ˆ์— ์ ์šฉํ•˜์—ฌ ๊ณ ํ•ด์ƒ๋„ LiDAR ์‹œ์Šคํ…œ์„ ๋งŒ๋“œ๋Š” ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. Galvanometer ์Šค์บ๋„ˆ ์ปจํŠธ๋กค๋Ÿฌ์™€ ์ž˜ ์•Œ๋ ค์ง„ ๋ž˜์Šคํ„ฐ ์Šค์บ” ๋ฐฉ๋ฒ•์— ๊ธฐ์ดˆํ•˜์—ฌ Galvanometer ์Šค์บ๋„ˆ๋ฅผ ๋ชจ๋ธ๋ง, ๋งค๊ฐœ๋ณ€์ˆ˜ํ™”ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌผ๋ฆฌ์ ์ธ ์Šค์บ๋‹ ์›€์ง์ž„๊ณผ ์ตœ์†Œ ์‹ ์žฅ ํŠธ๋ฆฌ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์Šค์บ๋‹ ๋ฐฉ๋ฒ•์„ ๋ชจ๋ธ๋งํ•˜๊ณ  ๋ถ„์„ํ•œ๋‹ค. ๋ถ„์„์œผ๋กœ๋ถ€ํ„ฐ ์›ํ•˜๋Š” FOV (field of view)๋กœ ์บก์ณ๋œ ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ๊ณผ ์Šค์บ๋‹ ์†๋„ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ฐํ˜”๋‹ค. ๋˜ํ•œ ํš๋“๋œ ์ด๋ฏธ์ง€๊ฐ€ FOV๋ฅผ ์™„์ „ํžˆ ํ‘œํ˜„ํ•˜๋ฉฐ, ์บก์ณ๋œ object๋“ค์ด ํŠน์ • ํ”„๋ ˆ์ž„ ๋ ˆ์ดํŠธ์—์„œ ์ž˜ ์ •๋ ฌ๋จ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ถฉ๋ถ„์กฐ๊ฑด์„ ์œ ๋„ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ œ์•ˆ๋œ ๊ฐœ๋…์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์ƒ˜ํ”Œ LIDAR ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋‘˜์งธ, 2๋‹จ๊ณ„ ์ƒ˜ํ”Œ๋ง์˜ ๋‹จ์ ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ๋‹ค์šด ์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ์—์„œ ์ƒ˜ํ”Œ๋ง ํŒจํ„ด์˜ ์ตœ์  ํ‘œํ˜„์„ ์ƒ์„ฑํ•จ์œผ๋กœ์จ ์—ฐ์‚ฐ ๋ณต์žก๋„์™€ ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ƒ˜ํ”Œ๋ง ํŒจํ„ด์€ ๋‹ค์šด ์ƒ˜ํ”Œ๋œ ํ‘œํ˜„์˜ k-NN ํ™•์žฅ ์—ฐ์‚ฐ์œผ๋กœ๋ถ€ํ„ฐ ๋„์ถœ๋œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋ฌผ์ฒด ๊ฒฝ๊ณ„ ๋˜๋Š” ๋ณต์žกํ•œ ํ…์Šค์ฒ˜์— ํ•œํ•ด์„œ ํ™•์žฅ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ๋ฌผ์ฒด ๊ฒฝ๊ณ„๋ฅผ ๋ณด์กดํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์„ ํ˜•์ ์ธ ์‹œ๊ฐ„ ๋ณต์žก๋„๋กœ ๋™์ž‘ํ•˜๋ฉฐ ๋‹ค์šด ์ƒ˜ํ”Œ๋ง ๋น„์œจ์„ ์ด์šฉํ•˜์—ฌ ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰์„ ์ค„์ธ๋‹ค. Middlebury ๋ฐ์ดํ„ฐ์…‹๊ณผ Brown laser-range ๋ฐ์ดํ„ฐ์…‹์„ ์‚ฌ์šฉํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๊ฐ€ ์ œ์‹œ๋œ๋‹ค. ์…‹์งธ, 2๋‹จ๊ณ„ ์ƒ˜ํ”Œ๋ง๊ณผ ๊ฐ™์€ ์ตœ์‹ ์˜ ์ ์‘์  ๋ฐฉ๋ฒ•๋“ค์€ ๋น„๊ต์  ๋‚ฎ์€ ์ƒ˜ํ”Œ๋ง ๋ ˆ์ดํŠธ๋กœ ์‹ค๋‚ด์˜ ๋ณต์žกํ•˜์ง€ ์•Š์€ ์žฅ๋ฉด๋“ค์„ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐ ๋งค์šฐ ํšจ๊ณผ์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณต์žกํ•œ ๋„๋กœ ํ™˜๊ฒฝ์—์„œ๋Š”, ํŠนํžˆ ์ธก์ • ์žฅ๋น„์˜ ์ƒ˜ํ”Œ๋ง ๋ ˆ์ดํŠธ๊ฐ€ ๋‚ฎ์€ ๊ฒฝ์šฐ์—, ํ•ด๋‹น ๋ฐฉ๋ฒ•๋“ค์˜ ์„ฑ๋Šฅ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋–จ์–ด์ง„๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์€ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ ๋„๋กœ ํ™˜๊ฒฝ์—์„œ์˜ ROI (region-of-interest) ๊ธฐ๋ฐ˜ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ƒ˜ํ”Œ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ CNN (convolutional neural network) ๊ธฐ๋ฐ˜์˜ ๋น ๋ฅด๊ณ  ์ •ํ™•ํ•œ ๋„๋กœ ๋ฐ ๋ฌผ์ฒด ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜์—ฌ, semantic ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๊ณ  ๋„๋กœ, ๋ฌผ์ฒด, ๋ฐฐ๊ฒฝ ์˜์—ญ์— ์ƒ˜ํ”Œ๋“ค์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ถ„๋ฐฐํ•œ๋‹ค. KITTI ๋ฐ์ดํ„ฐ์…‹์„ ์‚ฌ์šฉํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๊ฐ€ ์ œ์‹œ๋œ๋‹ค.Abstract i Table of Contents iii List of Figures vii List of Tables xi Chapter 1: Introduction ๏ผ‘ 1.1. Overview ๏ผ‘ 1.2. Scope and contributions ๏ผ’ 1.3. Thesis Outlines ๏ผ“ Chapter 2: Related work ๏ผ” 2.1. LiDAR sensors ๏ผ” 2.2. Sampling ๏ผ– 2.2.1. Sampling problem definition ๏ผ– 2.2.2. Sampling model ๏ผ— 2.2.3. Oracle Random sampling (Gradient-based sampling) ๏ผ˜ 2.3. Reconstruction ๏ผ™ Chapter 3: Dual-Mirror LiDAR ๏ผ‘๏ผ‘ 3.1. Introduction ๏ผ‘๏ผ‘ 3.1.1. Related work ๏ผ‘๏ผ’ 3.2. Modelling a controller of dual-mirror scanners ๏ผ‘๏ผ“ 3.2.1. Dual-mirror scanners ๏ผ‘๏ผ“ 3.2.2. Controller Model ๏ผ‘๏ผ• 3.2.2.1. FOV representation ๏ผ‘๏ผ• 3.2.2.2. Timing constraints ๏ผ‘๏ผ– 3.2.2.3. Maximum Speed of LiDAR scanners ๏ผ‘๏ผ— 3.3. LiDAR scanning optimization problem ๏ผ‘๏ผ˜ 3.3.1. Scanning Problem ๏ผ‘๏ผ™ 3.3.2. Optimal scanning pattern ๏ผ’๏ผ 3.3.2.1. Grid-graph representation of Field of View ๏ผ’๏ผ 3.3.2.2. Optimal scanning pattern ๏ผ’๏ผ‘ 3.3.2.3. Combining an optimal sampling pattern with timing constraints ๏ผ’4 3.4. LiDAR system Prototype ๏ผ“๏ผ 3.4.1. System overview ๏ผ“๏ผ 3.4.2. Speed evaluation ๏ผ“๏ผ’ 3.4.3. Subjective Evaluation ๏ผ“๏ผ“ 3.4.4. Accuracy Evaluation ๏ผ“๏ผ– Chapter 4: Sampling for Dual-Mirror LiDAR: Sampling Model and Algorithm ๏ผ“๏ผ˜ 4.1. Introduction ๏ผ“๏ผ˜ 4.2. Sampling Model for Dual-Mirror LiDAR ๏ผ”๏ผ‘ 4.2.1. Timing constraint ๏ผ”๏ผ‘ 4.2.2. Memory-space constraint ๏ผ”๏ผ• 4.2.3. New sampling problem with constraints ๏ผ”๏ผ— 4.3. Proposed sampling Algorithm and Its Properties ๏ผ”๏ผ˜ 4.3.1. Downsampling and k-NN expanding operator ๏ผ”๏ผ˜ 4.3.2. Proposed Sampling Algorithm with k-NN Expanding ๏ผ•๏ผ’ 4.3.3. Example with Synthetic Data ๏ผ•๏ผ— 4.3.4. Proposed sampling algorithm with interpolation ๏ผ•๏ผ™ 4.3.5. Timing and memory constraints ๏ผ–๏ผ’ 4.3.5.1. Timing constraint ๏ผ–๏ผ’ 4.3.5.2. Memory constraint ๏ผ–๏ผ“ 4.4. Experimental results ๏ผ–๏ผ” 4.4.1. Comparison on the conventional sampling problem ๏ผ–๏ผ• 4.4.1.1. Subjective comparison ๏ผ–๏ผ• 4.4.1.2. Quantitative comparison ๏ผ–๏ผ• 4.4.2. Comparison on the new sampling problem for LiDAR ๏ผ–๏ผ™ 4.4.2.1. Compression ratios ๏ผ–๏ผ™ 4.4.2.2. Quantitative evaluation with Peak-signal-to-noise-ratio ๏ผ—๏ผ 4.4.2.3. Quantitative evaluation with Percentages of bad pixels ๏ผ—๏ผ’ 4.4.3. Subjective evaluation ๏ผ—๏ผ— 4.4.4. Proposed grid sampling and grid sampling method ๏ผ—๏ผ™ 4.4.4.1. Middlebury datasets ๏ผ—๏ผ™ 4.4.4.2. Brown Laser range datasets ๏ผ˜๏ผ Chapter 5: ROI-based LiDAR Sampling in On-Road Environment for Autonomous Driving ๏ผ˜๏ผ” 5.1. Introduction ๏ผ˜๏ผ” 5.2. Proposed ROI-based sampling algorithm ๏ผ˜๏ผ— 5.2.1. Motivating example ๏ผ˜๏ผ— 5.2.2. ROI-based Sampling Problem ๏ผ™๏ผ‘ 5.2.3. Proposed ROI-based sampling algorithm ๏ผ™๏ผ“ 5.2.4. Practical considerations ๏ผ™๏ผ” 5.2.5. Distortion optimization problem ๏ผ™๏ผ• 5.3. Experimental results ๏ผ™๏ผ– 5.3.1. Datasets ๏ผ™๏ผ– 5.3.2. Evaluation with different parameters ๏ผ™๏ผ™ 5.3.3. Object and quantitative comparisons ๏ผ‘๏ผ๏ผ’ Chapter 6: Implementation Issues ๏ผ‘๏ผ๏ผ˜ 6.1. Implementation of gradient-based sampling ๏ผ‘๏ผ๏ผ˜ 6.2. System overview ๏ผ‘๏ผ‘๏ผ‘ Chapter 7: Conclusion ๏ผ‘๏ผ‘๏ผ“ References ๏ผ‘๏ผ‘๏ผ• ์ดˆ๋ก ๏ผ‘๏ผ’๏ผ”Docto

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