Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models

A tracking system that will be used for augmented reality applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay, occurring because of the delay in estimating the motion of the user and displaying images based on this estimate). This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS–IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context running at modest frame rates due to the design of the fusion algorithm

Teoria de confiabilidade generalizada para múltiplos outliers: apresentação, discussão e comparação com a teoria convencional

Após o ajustamento de observações pelo método dos mínimos quadrados (MMQ) ter sido realizado, é possível a detecção e a identificação de erros não aleatórios nas observações, por meio de testes estatísticos. A teoria da confiabilidade faz uso de medidas adequadas para quantificar o menor erro detectável em uma observação, e a sua influência sobre os parâmetros ajustados, quando não detectado. A teoria de confiabilidade convencional foi desenvolvida para os procedimentos de teste convencionais, como o data snooping, que pressupõem que apenas uma observação está contaminada por erros grosseiros por vez. Recentemente foram desenvolvidas medidas de confiabilidade generalizadas, relativas a testes estatísticos que pressupõem a existência, simultânea, de múltiplas observações com erros (outliers). O objetivo deste trabalho é apresentar, aplicar e discutir a teoria de confiabilidade generalizada para múltiplos outliers. Além da formulação teórica, este artigo também apresenta experimentos realizados em uma rede GPS (Global Positioning System), onde erros propositais foram inseridos em algumas observações e medidas de confiabilidade e testes estatísticos foram calculados utilizando a abordagem para múltiplos outliers. Comparações com a teoria de confiabilidade convencional também são realizadas. Por fim, apresentam-se as discussões e conclusões obtidas com estes experimentos

Planejamento de redes geodésicas resistentes a múltiplos outliers

Ao se planejar o levantamento de uma rede geodésica, deseja-se que as observações a serem realizadas e as coordenadas dos pontos a serem estimadas atendam critérios de precisão e confiabilidade pré-estabelecidos de acordo com os objetivos do projeto. Na etapa de pré-análise, antes mesmo da coleta das observações, é possível estimar a precisão e confiabilidade da rede, estipulando uma geometria/configuração para a mesma e a precisão esperada para as observações. O objetivo deste artigo é apresentar o planejamento de uma rede geodésica que atenda critérios de precisão e confiabilidade, considerando a possível existência de dois ou mais erros não detectados nas observações, bem como a influência (simultânea) destes erros sobre os parâmetros (coordenadas ajustadas dos vértices). Além da revisão teórica, experimentos foram realizados em uma rede GNSS, onde foram estipulados critérios de precisão e confiabilidade considerando a existência de até duas observações contaminadas por erros (outliers), de maneira simultânea. O planejamento da rede foi feito por meio do método da tentativa e erro. Depois do processamento dos dados e do ajustamento da rede, se verificou que os critérios de precisão e confiabilidade que foram estipulados na etapa de pré-análise foram devidamente obtidos

A COMPARATIVE ANALYSIS OF SPATIOTEMPORAL DATA FUSION MODELS FOR LANDSAT AND MODIS DATA

In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration

Sensitivity analysis of multiple fault test and reliability measures in integrated GPS/INS systems

Based on Kalman filtering, multi-sensor navigation systems, such as the integrated GPS/INS system, are widely accepted to enhance the navigation solution for various applications. However, such integrated systems do not always provide robust and stable navigation solutions due to unmodelled measurements and system dynamic errors, such as faults that degrade the performance of Kalman filtering for such integration. Single fault detection methods based on least squares (snapshot) method were investigated extensively in the literature and found effective to detect the fault at either sensor level or integration level. However, the system might be contaminated by multiple faults simultaneously. Thus, there is an increased likelyhood that some of the faults may not be detected and identified correctly. This will degrade the accuracy of positioning. In this paper multiple fault test and reliability measures based on a snapshot method were implemented in both the measurement model and the predicted states model for use in a GPS/INS integration system. The influences of the correlation coefficients between fault test statistics on the performances of the faults test and reliability measures were also investigated. The results indicate that the multiple fault test and reliability measures can perform more effectively in the measurement model than the predicted states model due to weak geometric strength within the predicted states model

Measurable Realistic Image-based 3D Mapping

This paper proposes and demonstrates a 3D map concept that is realistic and image-based, that enables geometric measurements and geo-location services. Additionally, image-based 3D maps provide more detailed information of the real world than 3D model-based maps. The image-based 3D maps use geo-referenced stereo images or panoramic images. The geometric relationships between objects in the images can be resolved from the geometric model of stereo images. The panoramic function makes 3D maps more interactive with users but also creates an interesting immersive circumstance. Actually, unmeasurable image-based 3D maps already exist, such as Google street view, but only provide virtual experiences in terms of photos. The topographic and terrain attributes, such as shapes and heights though are omitted. This paper also discusses the potential for using a low cost land Mobile Mapping System (MMS) to implement realistic image 3D mapping, and evaluates the positioning accuracy that a measureable realistic image-based (MRI) system can produce. The major contribution here is the implementation of measurable images on 3D maps to obtain various measurements from real scenes

Residual Based Adaptive Unscented Kalman Filter for Satellite Attitude Estimation

Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as the gyro and magnetometer biases. The adaptation is performed using a single adaptive factor calculated in the base of the residual sequence and the process noise covariance matrix tuned dynamically via multiplication with this factor. The simulation results demonstrate that the proposed Adaptive Unscented Kalman Filter (AUKF) outperforms the conventional UKF in the sense of estimation accuracy and convergence characteristics. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved