Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

Anbarasu, B.; Anitha, G.

doi:10.1017/s0373463319000420

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The major contribution of this study was the integration of proposed Ohta Color-GIST wavelet descriptors and CENTRIST (spatial pyramid representation) descriptors, to recognize complex indoor versus outdoor scenes for MAV navigation in GPS-denied indoor and outdoor environments. Finally, several additional scene recognition methods [20][21][22] have been proposed, for indoor versus outdoor scene and indoor scene categorization.…”

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confidence: 99%

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

Ganesan

Anbarasu

2019

Vis. Comput. Ind. Biomed. Art

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In the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)-95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs-2.08% and 4.92%, respectivelywere obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

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Section: Introductionmentioning

confidence: 99%

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

Ganesan

Anbarasu

2019

Vis. Comput. Ind. Biomed. Art

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Indoor 3D Positioning Based on Bluetooth Beacons

Grinyak¹,

Devyatisil’nyi²,

Shurygin³

2022

SMART Automatics and Energy

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Позиционирование в трехмерном пространстве внутри помещений по данным Bluetooth-маяков

Гриняк

2020

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Работа посвящена проблеме определения местоположения внутри помещений в тех случаях, когда сигнал спутниковых средств (GPS, Глонасс) недоступен или ограничен. В этом случае для определения координат используют другие источники навигационной информации. К ним относятся инерциальные навигационные средства (гироскопы, акселерометры), устройства для беспроводной передачи данных Wi-Fi и Bluetooth, магнитометр, датчик атмосферного давления, видеокамера и др. В настоящее время системы позиционирования внутри помещений на основе Bluetooth и Wi-Fi (по отдельности или совместно) получили наибольшее распространение. Использование для позиционирования Bluetooth маяков имеет целый ряд преимуществ. Самыми значимыми являются возможность произвольного размещения маяков из-за их автономности и малых размеров и возможность использования недорогого и распространённого оборудования, работа с которым не требует привлечения узких, редких и высокооплачиваемых специалистов. В статье рассматривается задача позиционирования объекта в трёхмерном пространстве по данным Bluetooth устройств, расположенных в помещении и образующих многопозиционную маячную систему наблюдения. Отмечается, что для успешной работы такой системы необходимо заранее оценивать характерную погрешность оценки координат позиционируемых объектов и при необходимости изменять конфигурацию маяков. Предлагается модельная интерпретация задачи позиционирования в виде системы алгебраических линейных уравнений. Такое представление позволяет строить априорные теоретические оценки погрешности определения координат объекта, определять области пространства, где точность позиционирования недостаточна. В работе приводятся данные расчётов ожидаемой точности решения задачи в различных характерных ситуациях и результаты натурных экспериментов, подтверждающих расчёты. В целом исследование оптимистично оценивает перспективы решения 3D задач позиционирования внутри помещений с помощью Bluetooth маяков. This paper devoted to research of indoors navigation problems under poor or insufficient quality of satellite navigational data environment. The problem of object positioning in 3D space by Bluetooth devices located indoors forming a multi-position tracking system is considered in this research. Emphasized that in order to succeed for such system it is required to pre-estimate distinctive accuracy. The proposed model interpretation of the positioning problem as the system of linear equations. The classic model interpretation for method of least squares is used for resolution. General problem of linearization around reference resolution is the locality of its features. There are three concepts of problems solvability, such as fundamental solvability (observability), solvability in conditions of instrumental measurement errors and solvability under conditions of finite accuracy of computation on a computer. The first aspect of solvability is interpreted by the completeness of the rank of the corresponding system of linear algebraic equations, the second and third ones represents by the conditionality of the problem and the convergence of the iterative estimation procedure. The conducted experiments show that for the positioning problem the attributes of the linearized model are accurate enough to represent the original nonlinear problem. Such interpretation allows to build theoretical accuracy estimation priors for object coordinates evaluations and to identify the areas with insufficient positioning accuracy. In this paper there are results of expected accuracy evaluation for various system patterns with full-scale experiments proving the theoretical calculations. Experiments for problems with using SKYLAB Beacon VG01 Bluetooth transmitters and smartphone HUAWEI WAS-LX1 are presented and confirmed that math model with linear approximation defined by authors is usable for solving indoors navigation problems using Bluetooth signal. So, for good enough quantity and appropriate location of the tracks the achievable positioning accuracy could be as good as 0.2-0.3 meters for all three coordinates. Such accuracy allows to navigate small hovering objects such as drones. In general, it looks promising to use Bluetooth trackers for solving positioning problems for indoors environments.

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Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

Cited by 3 publications

References 27 publications

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

Indoor 3D Positioning Based on Bluetooth Beacons

Позиционирование в трехмерном пространстве внутри помещений по данным Bluetooth-маяков

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