Embedded System for Visual Odometry and Localization of Moving Objects in Images Acquired by Unmanned Aerial Vehicles

Rebouças, Rodrigo Augusto; Eller, Quenaz da Cruz; Habermann, Mateus; Shiguemori, Elcio Hideiti

doi:10.1109/sbesc.2013.34

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Similarly, [11] outlines an embedded IPU developed to provide visual odometry and localization of moving objects for unmanned aerial vehicles. It ran feature detection and parameter estimation algorithms on an overclocked Raspberry Pi.…”

Section: Related Workmentioning

confidence: 99%

Image Processing Units on Ultra-low-cost Embedded Hardware: Algorithmic Optimizations for Real-time Performance

Nair¹,

Somani²,

Grunau

et al. 2017

J Sign Process Syst

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The design and development of image processing units (IPUs) has traditionally involved trade-offs between cost, real-time properties, portability, and ease of programming. A standard PC can be turned into an IPU relatively easily with the help of readily available computer vision libraries, but the end result will not be portable, and may be costly. Similarly, one can use field programmable gate arrays (FPGAs) as the base for an IPU, but they are expensive and require hardware-level programming. Finally, general purpose embedded hardware tends to be under-powered and difficult to develop for due to poor support for running advanced software. In recent years a new option has surfaced: single-board computers (SBCs). These generally inexpensive embedded devices would be attractive as a platform on which to develop IPUs due to their inherent portability and good compatibility with existing computer vision (CV) software. However, whether their performance is sufficient for real-time image processing has thus far remained an open question. Most SBCs (especially the ultra-low-cost ones which we target) do not offer CUDA/OpenCL support which makes it difficult to port GPU-based CV applications. In order to utilize the full power of the SBCs, their GPUs need to be used. In our attempts at doing this, we have observed that the CV algorithms which an IPU uses have to be re-designed according to the OpenGL support available Suraj Nair

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Section: Related Workmentioning

confidence: 99%

Image Processing Units on Ultra-low-cost Embedded Hardware: Algorithmic Optimizations for Real-time Performance

Nair¹,

Somani²,

Grunau

et al. 2017

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…Embedded systems have the advantage of small size and low power, are alsopopularlyused in mobile robots to decreaseweight and increase endurance [1]. In the promisingfield of mobile robotics, localization has been a hot topic in recentyears.…”

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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Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms

2022

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Embedded System for Visual Odometry and Localization of Moving Objects in Images Acquired by Unmanned Aerial Vehicles

Cited by 6 publications

References 14 publications

Image Processing Units on Ultra-low-cost Embedded Hardware: Algorithmic Optimizations for Real-time Performance

Image Processing Units on Ultra-low-cost Embedded Hardware: Algorithmic Optimizations for Real-time Performance

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

Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms

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