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

A neuro fuzzy embedded agent approach towards the development of an intelligent refrigerator

This paper aims to investigate the development of an intelligent refrigerator through embedding intelligent agents in normal refrigerators. The proposed intelligent refrigerator will be able to recognize various food items and learn user consumption habits via the proposed intelligent agent. The system incorporates a low-cost camera which feeds its input to the fuzzy agent in order to classify different food items and track their amounts. Using this information, consumption patterns representing the amount of items taken by users are generated and fed into a neural network system which is aimed to learn user habits and provide feedback to the user in cases where he/she consumes an unusual number of items. The system employs a second camera to distinguish between different users so that user specific information such as items consumed and calories taken can be stored separately. The resulting system has an accuracy of ≃ 90% for item identification and shows a very good performance for learning the habits of different users. The system also provides a graphical interface to display available items which allows users to generate an automated shopping list. © 2013 IEEE