FPGA implementation of HOOFR bucketing extractor-based real-time embedded SLAM applications

Nguyen, Dai Duong; Elouardi, Abdelhafid; Rodríguez, Sergio; Bouaziz, Samir

doi:10.1007/s11554-020-00986-9

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…erefore, this paper adopts the pipeline way to achieve the local adaptive threshold on FPGA. According to the requirements of equation ( 5), the following steps are required: (1) calculate μ in the processing window; (2) make the difference between the center pixel of the window din(x, y) and μ and calculate the square of the di erence (di n(x, y) − μ) 2 ; (3) multiply the result of step (2) by 225 to complete the calculation of the left inequality; (4) calculate the 225-pixel values in the processing window and complete the calculation of the inequality on the right with the square sum of μ; (5) compare steps ( 3) and (4) to complete the local adaptive threshold segmentation; (6) align the rows and columns, and complete the boundary processing.…”

Section: Fpga Implementationmentioning

confidence: 99%

“…e traditional image processing system based on software platforms has been di cult to meet the needs, so people put forward new requirements for the image processing system. Due to the natural parallelism of image processing algorithms, the addition of eld-programmable gate array (FPGA) hardware platforms has brought new vitality to image processing [1][2][3][4][5][6]. In addition, image segmentation is a very important image processing technology, especially in the medical eld.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Local Threshold Segmentation Based on FPGA

Gao

Wang

Chen

et al. 2022

Journal of Electrical and Computer Engineering

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In the process of the development of image processing technology, image segmentation is a very important image processing technology in the field of machine vision, pedestrian detection, medical imaging, and so on. However, the traditional image segmentation technology cannot solve the problems of reflection and uneven illumination. This paper presents a local threshold segmentation method based on FPGA, which can automatically select the optimal threshold according to different gray levels of images. First, the image is processed by mean filtering to remove noise interference in the image. Then, the idea of the mean value of the local neighborhood block and the Gaussian weighted sum in the local neighborhood is used to deal with the reflective and uneven light on the image. The process is designed and realized on FPGA. Finally, the design algorithm is verified by ModelSim simulation software and QT5 software. The experimental results show that the algorithm can effectively solve the problems of reflection and uneven illumination on the image surface, and the segmentation effect is significantly improved compared with the fixed threshold algorithm and Otsu algorithm. It also has certain reference value in medicine, agriculture, engineering, and other fields.

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Section: Fpga Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Local Threshold Segmentation Based on FPGA

Gao

Wang

Chen

et al. 2022

Journal of Electrical and Computer Engineering

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“…These developed systems require autonomous movement without the farmers' participation, making the algorithmic conception a complicated task. Several attempts have been made in the literature to propose localization and mapping algorithms in the automotive domain [19,20]. These algorithms have been adapted to control robots in the agricultural field.…”

Section: Introductionmentioning

confidence: 99%

Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

et al. 2022

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Our work is focused on developing an autonomous robot to monitor greenhouses and large fields. This system is designed to operate autonomously to extract useful information from the plants based on precise GPS localization. The proposed robot is based on an RGB camera for plant detection and a multispectral camera for extracting the different special bands for processing, and an embedded architecture integrating a Nvidia Jetson Nano, which allows us to perform the required processing. Our system uses a multi-sensor fusion to manage two parts of the algorithm. Therefore, the proposed algorithm was partitioned on the CPU-GPU embedded architecture. This allows us to process each image in 1.94 s in a sequential implementation on the embedded architecture. The approach followed in our implementation is based on a Hardware/Software Co-Design study to propose an optimal implementation. The experiments were conducted on a tomato farm, and the system showed that we can process different images in real time. The parallel implementation allows to process each image in 36 ms allowing us to satisfy the real-time constraints based on 5 images/s. On a laptop, we have a total processing time of 604 ms for the sequential implementation and 9 ms for the parallel processing. In this context, we obtained an acceleration factor of 66 for the laptop and 54 for the embedded architecture. The energy consumption evaluation showed that the prototyped system consumes a power between 4 W and 8 W. For this raison, in our case, we opted a low-cost embedded architecture based on Nvidia Jetson Nano.

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“…The HOOFR-SLAM algorithm uses images acquired by a stereo camera to performsimultaneouslocalization and mapping, the implementationwasbased on a CPU-GPU architecture using CUDA and OpenCL. The embedded platforms used are JETSON Tegra X1 equippedwith 4-Core ARM and A57 4-Core ARM A53 @ 1.3-1.9 GHz and Intel core i7 laptop @ 3.40 GHz [9].Recently, the authors in [10] based on HOOFR extractor , designed a wholefeature extraction system, dedicated for SLAM application takingintoaccount the bucketingmethod. also, theyprovided A hardware-software codesignapproach in order to implement the system on FPGA-basedheterogeneous architecture usingOpenCLprogramming…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU

et al. 2021

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Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.

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FPGA implementation of HOOFR bucketing extractor-based real-time embedded SLAM applications

Cited by 7 publications

References 34 publications

Design and Implementation of Local Threshold Segmentation Based on FPGA

Design and Implementation of Local Threshold Segmentation Based on FPGA

Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU

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