Wyniki wyszukiwania - Biblioteka Nauki

1

Experimental verification of a walking robot self - localization system with the kinect sensor

100%

Nowicki M. , Skrzypczyński P.

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tom Vol. 7, No. 4

42--51

EN

In this paper we investigate methods for self-localisation of a walking robot with the Kinect 3D active range sensor. The Iterative Closest Point (ICP) algorithm is considered as the basis for the computation of the robot rotation and translation between two viewpoints. As an alternative, a feature-based method for matching of 3D range data is considered, using the Normal Aligned Radial Feature (NARF) descriptors. Then, it is shown that NARFs can be used to compute a good initial estimate for the ICP algorithm, resulting in convergent estimation of the sensor egomotion. Experimental results are provided.

2

Mathematical Model of Errors of Odometry and Georeferencing Channels in Visual Correlation Extreme Navigation

86%

Mukhina M. P. , Kharchenko V. P.

Logistics and Transport

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2017

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tom Vol. 33, No. 1

31--36

EN

The mathematic model of errors in correlation with the extreme navigation system (CENS) is developed basing on odometry and geo-referencing channels. The realization of the model is done in Simulink, and based on regular and random components of additive noise. The results of simulations prove accumulation of errors for odometry errors and its mitigation in case of geo-referencing in periods of correction.

3

Mathematical Model of Errors of Odometry and Georeferencing Channels in Visual Correlation Extreme Navigation

86%

Mukhina M. P. , Kharchenko V. P.

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tom 33

31-36

EN

The mathematic model of errors in correlation with the extreme navigation system (CENS) is developed basing on odometry and geo-referencing channels. The realization of the model is done in Simulink, and based on regular and random components of additive noise. The results of simulations prove accumulation of errors for odometry errors and its mitigation in case of geo-referencing in periods of correction.

4

Optymalizacja parametrów systemu odometrii wizyjnej RGB-D metodami populacyjnymi

72%

Kostusiak A. , Skrzypczyński P.

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2018

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tom z. 196

461--470

PL

Dokładna lokalizacja jest bardzo ważna w wielu praktycznych zagadnieniach robotyki mobilnej. Często korzysta się z odometrii wizyjnej, obecnie powszechnie wykorzystującej kamery (sensory) RGB-D. W niniejszym artykule badana jest możliwość automatycznego doboru optymalnych parametrów prostego systemu odometrii wizyjnej RGB-D. Wykorzystano i porównano dwie metody optymalizacji oparte na populacji rozwiązań: algorytm genetyczny oraz algorytm roju cząstek. Zbadano wpływ poszczególnych parametrów na dokładność otrzymywanych estymat trajektorii sensora. Na tej podstawie wyciągnięto wnioski zarówno co do skuteczności i efektywności zastosowanych metod optymalizacji, jak i co do wpływu poszczególnych parametrów na dokładność estymat. Eksperymenty przeprowadzono przy wykorzystaniu publicznie dostępnych zestawów danych, aby zapewnić weryfikowalność prezentowanych wyników.

EN

In this paper, we investigate two population-based optimization methods as the means for optimization of the selected parameters in a visual odometry system using RGB-D data. One of the simplest and most used approaches to localization with RGB-D data is feature-based visual odometry that computes frame-to-frame rigid transformations of the sensor upon a sparse set of features and then concatenates these transformations into an estimate of the trajectory. This approach, yet simple, requires careful tuning of a number of parameters that control both the behavior of the feature detector, and the frame-to-frame rota-translation estimation algorithm. Therefore, we propose to employ robust and efficient soft computing optimization methods to find the best parameters for an exemplary RGB-D visual odometry system. We investigate and compare two approaches: the simple to implement particle swarm optimization algorithm, and a more complicated variant of the genetic algorithm. We seek a set of parameters that not only provide good results in terms of the estimated trajectory residual errors but are also applicable to different RGB-D datasets. Moreover, the optimization experiments make it possible to draw more general conclusions as to the role of particular building blocks of the visual odometry system (e.g. RANSAC) in achieving accurate trajectory estimates.