Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Abstract

Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, lightweight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a lightweight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

A parallel Canny edge detection algorithm based on OpenCL acceleration

“…The input image is preprocessed by convolution filter with Gaussian filter to remove noise and reduce the influence of noise on gradient calculation, so as to better realize the effect of edge detection image segmentation. Therefore, image preprocessing requires convolution of the original image and Gaussian mask, and the processed image is more blurred than the original, which is conducive to image edge detection [36].…”

A parallel Canny edge detection algorithm based on OpenCL acceleration

“…Therefore, image preprocessing requires convolution of the original image and Gaussian mask, and the processed image is more blurred than the original, which is conducive to image edge detection [35].…”

Construction of English Translation Teaching Model Based on Cloud Data Edge Detection Algorithm and Output-Oriented Method

“…Compared with input learning, output can stimulate students' desire and enthusiasm for learning, and achieve better learning efficiency. In other words, language teaching starts with an output task, and then students try their best to complete the output task [22]. In this process, students will not only realize the practical value of output tasks in improving cultural literacy and communicative competence, but also realize their own language skills insufficiency.…”