FPGA implementation of RANSAC algorithm for real-time image geometry estimation

Tang, Jia; Shaikh-Husin, Nasir; Sheikh, Usman Ullah

doi:10.1109/scored.2013.7002592

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…RANSAC has been implemented in FPGAs. In [ 120 ], an implementation for real-time affine geometry estimation is introduced. The main task chosen to be accelerated was the fitness scoring function, where the authors claim that the speed-up factor increases with input data size.…”

Section: Applicationsmentioning

confidence: 99%

RANSAC for Robotic Applications: A Survey

Martínez-Otzeta

Rodríguez-Moreno

Mendialdua

et al. 2022

Sensors

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Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.

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Section: Applicationsmentioning

confidence: 99%

RANSAC for Robotic Applications: A Survey

Martínez-Otzeta

Rodríguez-Moreno

Mendialdua

et al. 2022

Sensors

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“…A hardware architecture and organization of the RANSAC for feature-based image registration are proposed in [20]. Additionally, a hardware/software co-design platform of RANSAC algorithm for real-time affine geometry estimation is presented in [21]. The most intensive computation, i.e.…”

Section: Hardware Accelerated Ransac Approachesmentioning

confidence: 99%

FRANSAC: Fast RANdom Sample Consensus for 3D Plane Segmentation

Zeineldin¹,

El-Fishawy²

2017

IJCA

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Scene analysis is a prior stage in many computer vision and robotics applications. Thanks to recent depth camera, we propose a fast plane segmentation approach for obstacle detection in indoor environments. The proposed method Fast RANdom Sample Consensus (FRANSAC) involves three steps: data input, data preprocessing and 3D RANSAC. Firstly, range data, obtained from 3D camera, is converted into 3D point clouds. Next, a preprocessing stage is introduced where a pass through and voxel grid filters are applied. Finally, planes are estimated using a modified 3D RANSAC. The experimental results demonstrate that our approach can segment planes and detect obstacles about 7 times faster than the standard RANSAC without losing the discriminative power.

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“…RANSAC is an iterative algorithm to find the affine model that best describes the transformation of the two subsequent frames. Unlike the conventional RANSAC [38], this work uses an upper bound time to terminate RANSAC computation (similar to [39]) regardless of the number of iterations due to the real-time constraint as illustrated in Algorithm 1.…”

Section: Ransacmentioning

confidence: 99%

“…Accelerator. RANSAC hardware design in [39] is utilized in this work, which accelerates only fitness scoring step. As described in Algorithm 2, fitness scoring is an iterative process which performs similar computation to all data samples based on hypothesis model.…”

Section: Ransac Hardwarementioning

confidence: 99%

FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

Tang

Shaikh-Husin

Sheikh

et al. 2016

International Journal of Reconfigurable Computing

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Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird’s eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels’ resolution greyscale videos.

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FPGA implementation of RANSAC algorithm for real-time image geometry estimation

Cited by 9 publications

References 14 publications

RANSAC for Robotic Applications: A Survey

RANSAC for Robotic Applications: A Survey

FRANSAC: Fast RANdom Sample Consensus for 3D Plane Segmentation

FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

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