Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field

In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetometers. Instead of matching features such as walls and furniture using some variation of the ICP algorithm, the local magnetic field can be matched to provide loop-closure and global trajectory updates in a Gaussian Process (GP) SLAM framework. With a MEMS-based inertial measurement unit providing a continuous trajectory, and the matching of locally distinct magnetic field maps, experimental results in this paper show that a drift-free navigation solution in an indoor environment with millimetre-level accuracy can be achieved. The GP-SLAM approach presented can be formulated as a maximum a posteriori estimation problem and it can naturally perform loop-detection, feature-to-feature distance minimization, global trajectory optimization, and magnetic field map estimation simultaneously. Spatially continuous features (i.e. smooth magnetic field signatures) are used instead of discrete feature correspondences (e.g. point-to-point) as in conventional vision-based SLAM. These position updates from the ambient magnetic field also provide enough information for calibrating the accelerometer and gyroscope bias in-use. The only restriction for this method is the need for magnetic disturbances (which is typically not an issue indoors); however, no assumptions are required for the general motion of the sensor.Comment: ISPRS Workshop Indoor 3D 201

Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

Pozicioniranje i praćenje pješaka u zatvorenom prostoru koristeći senzore pametnih telefona, otkrivanje koraka i algoritam za geokodiranje

The paper deals with indoor navigation using inertial sensors (accelerometers, gyroscopes, etc.) built in a smartphone. The main disadvantage of the use of inertial sensors is the accuracy, which rapidly decreases with the increasing time of the measurement. The reason of the deteriorating accuracy is the presence of errors in inertial measurements, which are accumulated in the integration process. The paper describes the determination of a pedestrian trajectory using step detection method, which is improved with utilization of the adaptive step length estimation algorithm. This algorithm reflects the change of the step length with different types of movement. The proposal of the data processing uses information from floormap, what allows the verification of the pedestrian position and detects the collision of the trajectory with the floormap. The proposed algorithm significantly increases the accuracy of the resulting trajectory. Another extension of the proposed algorithm is the implementation of the barometer measurements for determination of the height differences. This fact allows change the floor in a multi-storey buildings. The experimental measurement was realized with a smartphone Samsung Galaxy S4.Rad se bavi navigacijom u zatvorenom prostoru koristeći inercijalne senzore (akcelerometre, žiroskope, itd.) ugrađene u pametne telefone. Najveći nedostatak korištenja inercijalnih senzora je netočnost koja se ubrzano povećava produljenjem vremena mjerenja. Razlog smanjenja točnosti je prisutnost pogrešaka inercijalnih mjerenja koje se akumuliraju kroz proces integracije. Rad opisuje određivanje putanje pješaka koristeći metodu praćenja koraka koja je poboljšana korištenjem algoritma za procjenu prilagodljive duljine koraka. Ovaj algoritam odražava promjene u duljini koraka s različitim vrstama kretanja. Prijedlog obrade podataka koristi informacije iz tlocrta katova što omogućava potvrdu položaja pješaka i otkriva koliziju putanje s tlocrtom. Predloženi algoritam znatno povećava točnost dobivene putanje. Drugi dodatak predloženog algoritma se odnosi na upotrebu barometarskih mjerenja pri određivanju visinskih razlika. Ova činjenica omogućava promjenu kata u višekatnoj zgradi. Eksperimentalno mjerenje je izvršeno uz pomoć pametnog telefona Samsung Galaxy S4