Building an enhanced vocabulary of the robot environment with a ceiling pointing camera

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set

An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values

Modeling the environment with egocentric vision systems

Cada vez más sistemas autónomos, ya sean robots o sistemas de asistencia, están presentes en nuestro día a día. Este tipo de sistemas interactúan y se relacionan con su entorno y para ello necesitan un modelo de dicho entorno. En función de las tareas que deben realizar, la información o el detalle necesario del modelo varía. Desde detallados modelos 3D para sistemas de navegación autónomos, a modelos semánticos que incluyen información importante para el usuario como el tipo de área o qué objetos están presentes. La creación de estos modelos se realiza a través de las lecturas de los distintos sensores disponibles en el sistema. Actualmente, gracias a su pequeño tamaño, bajo precio y la gran información que son capaces de capturar, las cámaras son sensores incluidos en todos los sistemas autónomos. El objetivo de esta tesis es el desarrollar y estudiar nuevos métodos para la creación de modelos del entorno a distintos niveles semánticos y con distintos niveles de precisión. Dos puntos importantes caracterizan el trabajo desarrollado en esta tesis: - El uso de cámaras con punto de vista egocéntrico o en primera persona ya sea en un robot o en un sistema portado por el usuario (wearable). En este tipo de sistemas, las cámaras son solidarias al sistema móvil sobre el que van montadas. En los últimos años han aparecido muchos sistemas de visión wearables, utilizados para multitud de aplicaciones, desde ocio hasta asistencia de personas. - El uso de sistemas de visión omnidireccional, que se distinguen por su gran campo de visión, incluyendo mucha más información en cada imagen que las cámara convencionales. Sin embargo plantean nuevas dificultades debido a distorsiones y modelos de proyección más complejos. Esta tesis estudia distintos tipos de modelos del entorno: - Modelos métricos: el objetivo de estos modelos es crear representaciones detalladas del entorno en las que localizar con precisión el sistema autónomo. Ésta tesis se centra en la adaptación de estos modelos al uso de visión omnidireccional, lo que permite capturar más información en cada imagen y mejorar los resultados en la localización. - Modelos topológicos: estos modelos estructuran el entorno en nodos conectados por arcos. Esta representación tiene menos precisión que la métrica, sin embargo, presenta un nivel de abstracción mayor y puede modelar el entorno con más riqueza. %, por ejemplo incluyendo el tipo de área de cada nodo, la localización de objetos importantes o el tipo de conexión entre los distintos nodos. Esta tesis se centra en la creación de modelos topológicos con información adicional sobre el tipo de área de cada nodo y conexión (pasillo, habitación, puertas, escaleras...). - Modelos semánticos: este trabajo también contribuye en la creación de nuevos modelos semánticos, más enfocados a la creación de modelos para aplicaciones en las que el sistema interactúa o asiste a una persona. Este tipo de modelos representan el entorno a través de conceptos cercanos a los usados por las personas. En particular, esta tesis desarrolla técnicas para obtener y propagar información semántica del entorno en secuencias de imágen

Virtual Reality as Navigation Tool: Creating Interactive Environments For Individuals With Visual Impairments

Research into the creation of assistive technologies is increasingly incorporating the use of virtual reality experiments. One area of application is as an orientation and mobility assistance tool for people with visual impairments. Some of the challenges are developing useful knowledge of the user’s surroundings and effectively conveying that information to the user. This thesis examines the feasibility of using virtual environments conveyed via auditory feedback as part of an autonomous mobility assistance system. Two separate experiments were conducted to study key aspects of a potential system: navigation assistance and map generation. The results of this research include mesh models that were fitted to the walk pathways of an environment, and collected data that provide insights on the viability of virtual reality based guidance systems

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig