Bayesian Programming for Topological Global Localization with Fingerprints

This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported

Topological Global Localization and Mapping with Fingerprint and Uncertainty

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported

Topology Learning and Place Recognition using Bayesian Programming for Mobile Robot Navigation

This paper proposes an approach allowing topology learning and recognition in indoor environments by using a probabilistic approach called Bayesian Programming. The main goal of this approach is to cope with the uncertainty, imprecision and incompleteness of handled information. The Bayesian Program for topology recognition and door detection is presented. The method has been successfully tested in indoor environments with the BIBA robot, a fully autonomous robot. The experiments address both the topology learning and topology recognition capabilities of the approach

Environmental Modeling with Fingerprint Sequences for Topological Global Localization

In this paper a perception approach allowing for high distinctiveness is presented. The method works in accordance to the fingerprint concept. Such representation allows using a very flexible matching approach based on the minimum energy algorithm. The whole extraction and matching approach is presented in details and viewed in a topological optic, where the matching result can directly be used as observation function for a topological localization approach. The experimentation section will validate the fingerprint approach and present different set of experiments in order to explain practically the choice of different types of features

Indoor localization using place and motion signatures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 141-153).Most current methods for 802.11-based indoor localization depend on either simple radio propagation models or exhaustive, costly surveys conducted by skilled technicians. These methods are not satisfactory for long-term, large-scale positioning of mobile devices in practice. This thesis describes two approaches to the indoor localization problem, which we formulate as discovering user locations using place and motion signatures. The first approach, organic indoor localization, combines the idea of crowd-sourcing, encouraging end-users to contribute place signatures (location RF fingerprints) in an organic fashion. Based on prior work on organic localization systems, we study algorithmic challenges associated with structuring such organic location systems: the design of localization algorithms suitable for organic localization systems, qualitative and quantitative control of user inputs to "grow" an organic system from the very beginning, and handling the device heterogeneity problem, in which different devices have different RF characteristics. In the second approach, motion compatibility-based indoor localization, we formulate the localization problem as trajectory matching of a user motion sequence onto a prior map. Our method estimates indoor location with respect to a prior map consisting of a set of 2D floor plans linked through horizontal and vertical adjacencies. To enable the localization system, we present a motion classification algorithm that estimates user motions from the sensors available in commodity mobile devices. We also present a route network generation method, which constructs a graph representation of all user routes from legacy floor plans. Given these inputs, our HMM-based trajectory matching algorithm recovers user trajectories. The main contribution is the notion of path compatibility, in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for metric/topological/semantic agreement with the prior map. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our method can recover the user's location to within several meters in one to two minutes after a "cold start."by Jun-geun Park.Ph.D

Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros

A real-time fingerprint-based indoor positioning using deep learning and preceding states

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase. In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research