<p><strong>Abstract.</strong> Many current indoor localisation approaches need an initial location at the beginning of localisation. The existing visual approaches to indoor localisation perform a 3D reconstruction of the indoor spaces beforehand, for determining this initial location, which is challenging for large indoor spaces. In this research, we present a visual approach for indoor localisation that is eliminating the requirement of any image-based reconstruction of indoor spaces by using a 3D model. A deep Bayesian convolutional neural network is fine-tuned with synthetic images generated from a 3D model to estimate the camera pose of real images. The uncertainty of the estimated camera poses is modelled by sampling the outputs of the Bayesian network fine-tuned with synthetic images. The results of the experiments indicate that a localisation accuracy of 2 metres can be achieved using the proposed approach.</p>
Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information from synthetic image sequences to improve localisation accuracy. A deep Bayesian recurrent CNN is fine-tuned using synthetic image sequences obtained from a building information model (BIM) to regress the pose of real image sequences. The results of the experiments indicate that the proposed approach estimates a smoother trajectory with smaller inter-frame error as compared to existing methods. The achievable accuracy with the proposed approach is 1.6 m, which is an improvement of approximately thirty per cent compared to the existing approaches. A Keras implementation can be found in our Github repository.
The healthcare system that prevailed some years ago was a mere pen and paper based system. A number of workers, staff, and written records were the main components of the prevailing system of healthcare. This had a number of drawbacks, and a number of mishaps occurred due to mismanagement of data and information. There was a need for development. Then, the concept of telemedicine came, which revolutionized the healthcare paradigm to a great extent. With the advancement of telemedicine, many major problems of the prevailing system were removed. But, still there were many other aspects which could be further improved to make healthcare facilities more enhanced. Keeping this in mind, the concept of Multi Agent System (MAS) was introduced in the healthcare system later. MASes are considered as the best and most appropriate technology that can be used in the development of applications in healthcare paradigm where the presence of multiple agents, heterogeneous and loosely coupled components, the data management in a dynamic and distributed environment, and multi-user collaborations are considered the most pertinent requirements for healthcare system. This chapter focuses mainly about MAS, its applications, and some systems that were developed by the authors.
The healthcare system that prevailed some years ago was a mere pen and paper based system. A number of workers, staff, and written records were the main components of the prevailing system of healthcare. This had a number of drawbacks, and a number of mishaps occurred due to mismanagement of data and information. There was a need for development. Then, the concept of telemedicine came, which revolutionized the healthcare paradigm to a great extent. With the advancement of telemedicine, many major problems of the prevailing system were removed. But, still there were many other aspects which could be further improved to make healthcare facilities more enhanced. Keeping this in mind, the concept of Multi Agent System (MAS) was introduced in the healthcare system later. MASes are considered as the best and most appropriate technology that can be used in the development of applications in healthcare paradigm where the presence of multiple agents, heterogeneous and loosely coupled components, the data management in a dynamic and distributed environment, and multi-user collaborations are considered the most pertinent requirements for healthcare system. This chapter focuses mainly about MAS, its applications, and some systems that were developed by the authors.
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