Query: Indoor navigation

title: ASSIST: Personalized Indoor Navigation via Multimodal Sensors and High Level Semantic Information
abstract: Blind visually impaired (BVI) individuals and those with Autism Spectrum Disorder (ASD) each face unique challenges in navigating unfamiliar indoor environments. In this paper, we propose an indoor positioning and navigation system that guides a user from point A to point B indoors with high accuracy while augmenting their situational awareness. This system has three major components: location recognition (a hybrid indoor localization app that uses Bluetooth Low Energy beacons and Google Tango to provide high accuracy) object recognition (a body mounted camera to provide the user momentary situational awareness of objects and people) and semantic recognition (map based annotations to alert the user of static environmental characteristics) This system also features personalized interfaces built upon the unique experiences that both BVI and ASD individuals have in indoor wayfinding and tailors its multimodal feedback to their needs. Here, the technical approach and implementation of this system are discussed, and the results of human subject tests with both BVI and ASD individuals are presented. In addition, we discuss and show the system's user centric interface and present points for future work and expansion.

title: Smartphone based Indoor Localization for Blind Navigation across Building Complexes
abstract: Continuous and accurate smartphone based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, they are still not ready for deployment in large and complex environments, like shopping malls and hospitals, where navigation assistance is needed. To achieve accurate, continuous, and real time localization with smartphones in such environments, we present a series of key techniques enhancing a probabilistic localization algorithm. The algorithm is designed for smartphones and employs inertial sensors on a mobile device and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 \text{m}{2} shopping mall which includes three multi story buildings and a large open underground passageway. Experiments in this space validate the effect of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

title: An indoor navigation architecture using variable data sources for blind and visually impaired persons
abstract: Contrary to outdoor positioning and navigation systems, there isn't a counterpart global solution for indoor environments. Usually, the deployment of an indoor positioning system must be adapted case by case, according to the infrastructure and the objective of the localization. A particularly delicate case is related with persons who are blind or visually impaired. A robust and easy to use indoor navigation solution would be extremely useful, but this would also be particularly difficult to develop, given the special requirements of the system that would have to be more accurate and user friendly than a general solution. This paper presents a contribute to this subject, by proposing a hybrid indoor positioning system adaptable to the surrounding indoor structure, and dealing with different types of signals to increase accuracy. This would permit lower the deployment costs, since it could be done gradually, beginning with the likely existing Wi Fi infrastructure to get a fairy accuracy up to a high accuracy using visual tags and NFC tags when necessary and possible.

title: Attitude estimation for indoor navigation and augmented reality with smartphones
abstract: We investigate the precision of attitude estimation algorithms in the particular context of pedestrian navigation with commodity smartphones and their inertial/magnetic sensors. We report on an extensive comparison and experimental analysis of existing algorithms. We focus on typical motions of smartphones when carried by pedestrians. We use a precise ground truth obtained from a motion capture system. We test state of the art and built in attitude estimation techniques with several smartphones, in the presence of magnetic perturbations typically found in buildings. We discuss the obtained results, analyze advantages and limits of current technologies for attitude estimation in this context. Furthermore, we propose a new technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We show how our technique compares and improves over previous works. A particular attention was paid to the study of attitude estimation in the context of augmented reality motions when using smartphones.

title: Travi Navi: Self Deployable Indoor Navigation System
abstract: We present Travi Navi a vision guided navigation system that enables a self motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi Navi also finds shortcuts whenever possible. In this paper, we describe the key techniques to solve several practical challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi Navi and conduct extensive experiments. The evaluation results show that Travi Navi can track and navigate users with timely instructions, typically within a four step offset, and detect deviation events within nine steps. We also characterize the power consumption of Travi Navi on various mobile phones.

title: Augmented Reality Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass
abstract: Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented reality based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head mounted device. In particular, we examined Google Glass as a wearable head mounted device in comparison with handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.

title: Error Modelling for Multi Sensor Measurements in Infrastructure Free Indoor Navigation
abstract: The long term objective of our research is to develop a method for infrastructure free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot mounted Inertial Measurement Unit (IMU) sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro Electro Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF) which is a sophisticated option for integrating measurements emerging from pedestrian motion having non Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university's premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized.

title: Supporting Indoor Navigation Using Access Rights to Spaces Based on Combined Use of IndoorGML and LADM Models
abstract: The aim of this research is to investigate the combined use of IndoorGML and the Land Administration Domain Model (LADM) to define the accessibility of the indoor spaces based on the ownership and/or the functional right for use. The users of the indoor spaces create a relationship with the space depending on the type of the building and the function of the spaces. The indoor spaces of each building have different usage functions and associated users. By defining the user types of the indoor spaces, LADM makes it possible to establish a relationship between the indoor spaces and the users. LADM assigns rights, restrictions, and responsibilities to each indoor space, which indicates the accessible spaces for each type of user. The three dimensional (3D) geometry of the building will be impacted by assigning such functional rights, and will provide additional knowledge to path computation for an individual or a group of users. As a result, the navigation process will be more appropriate and simpler because the navigation path will avoid all of the non accessible spaces based on the rights of the party. The combined use of IndoorGML and LADM covers a broad range of information classes: (indoor 3D) cell spaces, connectivity, spatial units/boundaries, (access/use) rights and restrictions, parties/persons/actors, and groups of them. The new specialized classes for individual students, individual staff members, groups of students, groups of staff members are able to represent cohorts of education programmes and the organizational structure (organogram: faculty, department, group) The model is capable to represent the access times to lecture rooms (based on education/teaching schedules) use rights of meeting rooms, opening hours of offices, etc. The two original standard models remain independent in our approach, we do not propose yet another model, but applications can fully benefit of the potential of the combined use, which is an important contribution of this paper. The main purpose of the combined use model is to support the indoor navigation, but could also support different applications, such as the maintenance and facility management work, by computing the cleaning cost based on the space floor area. The main contributions of this paper are: a solution for the combined use of IndoorGML LADM model, a conceptual enhancement of LADM by the refinement of the LA_Party package with specialization for staff and student (groups) and the assessment of the model by converting sample data (from two complex university buildings) into the model, and conducting actual access rights aware navigation, based on the populated model.

title: A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real Time Pipeline Classification
abstract: Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point to point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real time application in industrial plant engineering.

title: Achieving Practical and Accurate Indoor Navigation for People with Visual Impairments
abstract: Methods that provide accurate navigation assistance to people with visual impairments often rely on instrumenting the environment with specialized hardware infrastructure. In particular, approaches that use sensor networks of Bluetooth Low Energy (BLE) beacons have been shown to achieve precise localization and accurate guidance while the structural modifications to the environment are kept at minimum. To install navigation infrastructure, however, a number of complex and time critical activities must be performed. The BLE beacons need to be positioned correctly and samples of Bluetooth signal need to be collected across the whole environment. These tasks are performed by trained personnel and entail costs proportional to the size of the environment that needs to be instrumented. To reduce the instrumentation costs while maintaining a high accuracy, we improve over a traditional regression based localization approach by introducing a novel, graph based localization method using Pedestrian Dead Reckoning (PDR) and particle filter. We then study how the number and density of beacons and Bluetooth samples impact the balance between localization accuracy and set up cost of the navigation environment. Studies with users show the impact that the increased accuracy has on the usability of our navigation application for the visually impaired.