Efficient CORDIC Iteration Design of LiDAR Sensors’ Point-Cloud Map Reconstruction Technology

Fan, Yu-Cheng; Liu, Yicheng; Chu, Chiao-An

doi:10.3390/s19245412

Cited by 18 publications

(13 citation statements)

References 26 publications

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“…The value of the trigonometric functions can be found in seven rotations regardless of the number of input N digits [6]. The number of iterations are reduced by more than half [6]. 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 [6].…”

Section: Automotive Sensor Applicationsmentioning

confidence: 80%

“…Y.-C. Fan et al [6] proposed an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors [6,7]. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR sensors [8].…”

Section: Automotive Sensor Applicationsmentioning

confidence: 99%

“…A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR sensors [8]. The value of the trigonometric functions can be found in seven rotations regardless of the number of input N digits [6]. The number of iterations are reduced by more than half [6].…”

Section: Automotive Sensor Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Emerging Sensing Technologies in Consumer Electronics

Fan

2021

Sensors

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Section: Automotive Sensor Applicationsmentioning

confidence: 80%

Section: Automotive Sensor Applicationsmentioning

confidence: 99%

Section: Automotive Sensor Applicationsmentioning

confidence: 99%

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Emerging Sensing Technologies in Consumer Electronics

Fan

2021

Sensors

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“…KITTI dataset has been used for testing part in object recognition, to identify the categories of objects by using neural network. Dataset provides two kinds: 45°LiDAR point cloud and color images are taken in real scenes and PASCAL VOC dataset contains 20 classes, 9963 (2007) and 23080 (2012) images, respectively [22]. The number of objects is 24640 and 54900.…”

Section: Experiments and Analysis Of Resultsmentioning

confidence: 99%

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

et al. 2021

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Recently, self-driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self-driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self-driving cars more. Later on, using these introduced design models, a lot of companies started to design self-driving cars. Various sensors, such as radar, high-resolution cameras, and LiDAR are important in self-driving cars to sense the surroundings. LiDAR acts as an eye of a self-driving vehicle, by offering 64 scanning channels, 26.9° vertical field view, and a high-precision 360° horizontal field view in real-time. The LiDAR sensor can provide 360° environmental depth information with a detection range of up to 120 meters. In addition, the left and right cameras can further assist in obtaining front image information. In this way, the surrounding environment model of the self-driving car can be accurately obtained, which is convenient for the self-driving algorithm to perform route planning. It is very important for self-driving to avoid the collision. LiDAR provides both horizontal and vertical field views and helps in avoiding collision. In an online website, the dataset provides different kinds of data like point cloud data and color images which helps this data to use for object recognition. In this paper, we used two types of publicly available datasets, namely, KITTI and PASCAL VOC. Firstly, the KITTI dataset provides in-depth data knowledge for the LiDAR segmentation (LS) of objects obtained through LiDAR point clouds. The performance of object segmentation through LiDAR cloud points is used to find the region of interest (ROI) on images. And later on, we trained the network with the PASCAL VOC dataset used for object detection by the YOLOv4 neural network. To evaluate, we used the region of interest image as input to YOLOv4. By using all these technologies, we can segment and detect objects. Our algorithm ultimately constructs a LiDAR point cloud at the same time; it also detects the image in real-time.

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“…Accuracy is another common design evaluation metric alongside performance and resource usage. Cordic performs simple computations using only addition, subtraction, bit shifting, and table lookups, which are efficient to implement in FPGAs and more generally in hardware [38].…”

Section: Triangle Functionmentioning

confidence: 99%

Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array

Song

Zhu

Nan

et al. 2020

Sensors

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The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array.

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Efficient CORDIC Iteration Design of LiDAR Sensors’ Point-Cloud Map Reconstruction Technology

Cited by 18 publications

References 26 publications

Emerging Sensing Technologies in Consumer Electronics

Emerging Sensing Technologies in Consumer Electronics

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array

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