Stream-Based ORB Feature Extractor with Dynamic Power Optimization

Tran, Phong D.; Pham, Thinh Hung; Lam, Siew-Kei; Wu, Meiqing; Jasani, Bhavan

doi:10.1109/fpt.2018.00024

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…There are many FPGA-based implementations of binary descriptors. Sun et al [11], [23], de Lima et al [24], Liu et al [10], and Tran et al [25] implement the ORB algorithm on hardware. Fang et al [26] implement ORB algorithm on an FPGA for two scales of 640 × 480 and 533 × 400.…”

Section: B Fpga-based Implementations Of Binary Descriptorsmentioning

confidence: 99%

A Fully Pipelined FPGA Architecture for Multiscale BRISK Descriptors With a Novel Hardware-Aware Sampling Pattern

Ghaffari

Capson

2022

IEEE Trans. VLSI Syst.

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Binary descriptors have been shown to be faster than nonbinary descriptors while producing comparable results in image matching applications. In recent years, there have been many attempts to design hardware accelerators for extraction of binary descriptors to achieve higher processing rates. One of the well-known methods is the binary robust invariant scalable key point (BRISK) algorithm, which has shown outstanding results in various applications. In this work, we propose a multiscale fieldprogrammable gate array (FPGA)-based hardware architecture for the BRISK descriptor. In addition, a new image sampling pattern for the BRISK algorithm is described which is shown to be more efficient than the original sampling pattern for hardware implementation. Our new sampling pattern decreases the size of the patches containing the key point to one-quarter of the size of that used in the original BRISK algorithm, which leads to a reduction in FPGA resource utilization while maintaining the accuracy of the image matching application. Our proposed design is fully pipelined and achieves a frame rate of 78 fps on images with full HD resolution.

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Section: B Fpga-based Implementations Of Binary Descriptorsmentioning

confidence: 99%

A Fully Pipelined FPGA Architecture for Multiscale BRISK Descriptors With a Novel Hardware-Aware Sampling Pattern

Ghaffari

Capson

2022

IEEE Trans. VLSI Syst.

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“…They achieved 72fps on 1920x1080 images, processing the image at 1 pixel/cycle. Tran et al [22] propose a stream-based ORB accelerator focusing on dynamic power optimizations using dynamic clock gating, and threshold-guided bit-width pruning of intermediate computation values. No performance numbers were reported.…”

Section: Related Workmentioning

confidence: 99%

“…No performance numbers were reported. Both referenced studies [22,24] use the Harris Corner Detection algorithm instead of FAST for corner detection. Although Harris Corner is more complex computationally, it provides better accuracy in corner extraction.…”

Section: Related Workmentioning

confidence: 99%

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ORB-based SLAM accelerator on SoC FPGA

Vemulapati¹,

Chen²

2022

Preprint

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Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accuracy. ORB-SLAM is an opensourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an SoC based ORB-SLAM system that accelerates the computationally intensive visual feature extraction and matching on hardware. Our FPGA system based on a Zynq-family SoC runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA.

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“…The matching quality of the image pairs will directly affect the autonomous navigation and modeling quality of the subsequent mobile platform. To improve the performance of the ORB algorithm, researchers have mainly applied the following approaches: (1) improving the feature point extraction strategy [7], [13]- [19], (2) enhancing the feature descriptor [20]- [22], and (3) upgrading the strategy of matching feature points [23]- [28]. For example, to improve the distribution uniformity of feature points, enhance the matching efficiency, and increase the correct matching rate, the ORB-slam2 [29] initial matching algorithm boosts the feature point matching efficiency; this improvement is achieved by rasterizing the entire image and modifying the minimum Hamming matching distance to 0.7-0.9 times the sub-optimal matching distance; moreover, the histogram statistics are used to describe the sub-rotation main direction angle, and only the top three direction feature points are selected in the statistics to improve the correct matching rate.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Matching Approach for Mobile Platform Visual Imagery

et al. 2019

IEEE Access

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Unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images. Thus, accurate, stable, and well-distributed matches to follow the accurate pose estimation of the platform are difficult to obtain. To solve such problems, an effective image matching method for mobile platform visual imagery is presented in this study. The proposed method includes three steps, namely, standard initial matching, transformation matrices evaluation and matching propagation. Firstly, an oriented FAST and rotated BRIEF (ORB) method was used to obtain the number of matches and the initial projective transformation relationship between an image pair. Secondly, an evaluation function was set to choose the suitable rotation matrix for the image scene. Finally, geometric correspondence matching was utilized to propagate matches and produce additional reliable matching results. The geometric correspondence matching used the geometric relationship between the image pair and found more suitable matches than the standard ORB matching. Comprehensive experiments on TUM and ICL-NUIM dataset images showed that the proposed algorithm performs better in terms of correct matches, satisfactory matching rate, and higher matching accuracy than the standard ORB and ORB-slam2 initial match methods. INDEX TERMS Mobile platform visual imagery, image matching, ORB, propagation matching, geometric correspondence matching.

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Stream-Based ORB Feature Extractor with Dynamic Power Optimization

Cited by 10 publications

References 23 publications

A Fully Pipelined FPGA Architecture for Multiscale BRISK Descriptors With a Novel Hardware-Aware Sampling Pattern

A Fully Pipelined FPGA Architecture for Multiscale BRISK Descriptors With a Novel Hardware-Aware Sampling Pattern

ORB-based SLAM accelerator on SoC FPGA

Multi-Stage Matching Approach for Mobile Platform Visual Imagery

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