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- Merz, Mariann, et al.
(author)
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Autonomous UAS-Based Agriculture Applications : General Overview and Relevant European Case Studies
- 2022
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In: DRONES. - : MDPI. - 2504-446X. ; 6:5
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Journal article (peer-reviewed)abstract
- Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.
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- Ahlberg, Carl
(author)
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Embedded high-resolution stereo-vision of high frame-rate and low latency through FPGA-acceleration
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Autonomous agents rely on information from the surrounding environment to act upon. In the array of sensors available, the image sensor is perhaps the most versatile, allowing for detection of colour, size, shape, and depth. For the latter, in a dynamic environment, assuming no a priori knowledge, stereo vision is a commonly adopted technique. How to interpret images, and extract relevant information, is referred to as computer vision. Computer vision, and specifically stereo-vision algorithms, are complex and computationally expensive, already considering a single stereo pair, with results that are, in terms of accuracy, qualitatively difficult to compare. Adding to the challenge is a continuous stream of images, of a high frame rate, and the race of ever increasing image resolutions. In the context of autonomous agents, considerations regarding real-time requirements, embedded/resource limited processing platforms, power consumption, and physical size, further add up to an unarguably challenging problem.This thesis aims to achieve embedded high-resolution stereo-vision of high frame-rate and low latency, by approaching the problem from two different angles, hardware and algorithmic development, in a symbiotic relationship. The first contributions of the thesis are the GIMME and GIMME2 embedded vision platforms, which offer hardware accelerated processing through FGPAs, specifically targeting stereo vision, contrary to available COTS systems at the time. The second contribution, toward stereo vision algorithms, is twofold. Firstly, the problem of scalability and the associated disparity range is addressed by proposing a segment-based stereo algorithm. In segment space, matching is independent of image scale, and similarly, disparity range is measured in terms of segments, indicating relatively few hypotheses to cover the entire range of the scene. Secondly, more in line with the conventional stereo correspondence for FPGAs, the Census Transform (CT) has been identified as a recurring cost metric. This thesis proposes an optimisation of the CT through a Genetic Algorithm (GA) - the Genetic Algorithm Census Transform (GACT). The GACT shows promising results for benchmark datasets, compared to established CT methods, while being resource efficient.
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- Ahlberg, Carl, et al.
(author)
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GIMME - A General Image Multiview Manipulation Engine
- 2011
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In: Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig 2011). - Los Alamitos, Calif : IEEE Computer Society. - 9780769545516
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Conference paper (peer-reviewed)abstract
- This paper presents GIMME (General Image Multiview Manipulation Engine), a highly flexible reconfigurable stand-alone mobile two-camera vision platform with stereo-vision capability. GIMME relies on reconfigurable hardware (FPGA) to perform application-specific low to medium-level image-processing at video rate. The Qseven-extension enables additional processing power. Thanks to its compact design, low power consumption and standardized interfaces (power and communication), GIMME is an ideal vision platform for autonomous and mobile robot applications.
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- Ahlberg, Carl, et al.
(author)
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GIMME2 - An embedded system for stereo vision and processing of megapixel images with FPGA-acceleration
- 2015
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In: 2015 International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015. - 9781467394062
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Conference paper (peer-reviewed)abstract
- This paper presents GIMME2, an embedded stereovision system, designed to be compact, power efficient, cost effective, and high performing in the area of image processing. GIMME2 features two 10 megapixel image sensors and a Xilinx Zynq, which combines FPGA-fabric with a dual-core ARM CPU on a single chip. This enables GIMME2 to process video-rate megapixel image streams at real-time, exploiting the benefits of heterogeneous processing.
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- Ahlberg, Carl, et al.
(author)
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The Black Pearl: An Autonomous Underwater Vehicle
- 2013
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Reports (other academic/artistic)abstract
- The Black Pearl is a custom made autonomous underwater vehicle developed at Mälardalen University, Sweden. It is built in a modular fashion, including its mechanics, electronics and software. After a successful participation at the RoboSub competition in 2012 and winning the prize for best craftsmanship, this year we made minor improvements to the hardware, while the focus of the robot's evolution shifted to the software part. In this paper we give an overview of how the Black Pearl is built, both from the hardware and software point of view.
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- Ahlberg, Carl, et al.
(author)
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The genetic algorithm census transform : evaluation of census windows of different size and level of sparseness through hardware in-the-loop training
- 2021
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In: Journal of Real-Time Image Processing. - : SPRINGER HEIDELBERG. - 1861-8200 .- 1861-8219. ; :3, s. 539-559
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Journal article (peer-reviewed)abstract
- Stereo correspondence is a well-established research topic and has spawned categories of algorithms combining several processing steps and strategies. One core part to stereo correspondence is to determine matching cost between the two images, or patches from the two images. Over the years several different cost metrics have been proposed, one being the Census Transform (CT). The CT is well proven for its robust matching, especially along object boundaries, with respect to outliers and radiometric differences. The CT also comes at a low computational cost and is suitable for hardware implementation. Two key developments to the CT are non-centric and sparse comparison schemas, to increase matching performance and/or save computational resources. Recent CT algorithms share both traits but are handcrafted, bounded with respect to symmetry, edge lengths and defined for a specific window size. To overcome this, a Genetic Algorithm (GA) was applied to the CT, proposing the Genetic Algorithm Census Transform (GACT), to automatically derive comparison schemas from example data. In this paper, FPGA-based hardware acceleration of GACT, has enabled evaluation of census windows of different size and shape, by significantly reducing processing time associated with training. The experiments show that lateral GACT windows produce better matching accuracy and require less resources when compared to square windows.
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| 10. |
- Ahlberg, Carl, et al.
(author)
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Unbounded Sparse Census Transform using Genetic Algorithm
- 2019
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In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV). - : IEEE. - 9781728119755 ; , s. 1616-1625
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Conference paper (peer-reviewed)abstract
- The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.
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