Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

Evocative computing – creating meaningful lasting experiences in connecting with the past

We present an approach – evocative computing – that demonstrates how ‘at hand’ technologies can be ‘picked up’ and used by people to create meaningful and lasting experiences, through connecting and interacting with the past. The approach is instantiated here through a suite of interactive technologies configured for an indoor-outdoor setting that enables groups to explore, discover and research the history and background of a public cemetery. We report on a two-part study where different groups visited the cemetery and interacted with the digital tools and resources. During their activities serendipitous uses of the technology led to connections being made between personal memo-ries and ongoing activities. Furthermore, these experiences were found to be long-lasting; a follow-up study, one year later, showed them to be highly memorable, and in some cases leading participants to take up new directions in their work. We discuss the value of evocative computing for enriching user experiences and engagement with heritage practices

Augmented indoor hybrid maps using catadioptric vision

En este Trabajo de Fin de Máster se presenta un nuevo método para crear mapas semánticos a partir de secuencias de imágenes omnidireccionales. El objetivo es diseñar el nivel superior de un mapa jerárquico: mapa semántico o mapa topológico aumentado, aprovechando y adaptando este tipo de cámaras. La segmentación de la secuencia de imágenes se realiza distinguiendo entre Lugares y Transiciones, poniendo especial énfasis en la detección de estas Transiciones ya que aportan una información muy útil e importante al mapa. Dentro de los Lugares se hace una clasificación más detallada entre pasillos y habitaciones de distintos tipos. Y dentro de las Transiciones distinguiremos entre puertas, jambas, escaleras y ascensores, que son los principales tipos de Transiciones que aparecen en escenarios de interior. Para la segmentación del espacio en estos tipos de áreas se han utilizado solo descriptores de imagen globales, en concreto Gist. La gran ventaja de usar este tipo de descriptores es la mayor eficiencia y compacidad frente al uso de descriptores locales. Además para mantener la consistencia espacio-temporal de la secuencia de imágenes, se hace uso de un modelo probabilístico: Modelo Oculto de Markov (HMM). A pesar de la simplicidad del método, los resultados muestran cómo es capaz de realizar una segmentación de la secuencia de imágenes en clusters con significado para las personas. Todos los experimentos se han llevado a cabo utilizando nuestro nuevo data set de imágenes omnidireccionales, capturado con una cámara montada en un casco, por lo que la secuencia sigue el movimiento de una persona durante su desplazamiento dentro de un edificio. El data set se encuentra público en Internet para que pueda ser utilizado en otras investigaciones

Semantic Localization and Mapping in Robot Vision

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks. This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence. The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features. Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system. These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

Interstice

When I was about three years old, I distinctly remember being too small to see what was on top of the table. A couple of years later, when I could see those objects, I thought the world around me had grown smaller. In a way, it did, as I experienced, lived, captured, remembered, and shared the space repeatedly. This sense of the world shrinking was exaggerated during the Covid-19 pandemic, allowing new behaviours and modes of interaction to emerge. Continually shaping our modern lives, virtual technologies redefine how we access and share information and stories or even explore new places. Thanks to the exponential increase in our computing powers, we live in hyper- connectivity, constantly in sync with our multiple screens, tabs, devices and profiles. Smartphones serve as two- way communication bringing the world in and letting the home out. As people increasingly rely on digital tools for work, communication, and leisure, the boundaries between physical and digital realms have become blurred. The overlap of our stories, information and spaces has subsequently led to challenges in managing the clutter and disorganization that can arise in physical and digital realms, affecting productivity, well-being, and overall user experience. In response, this thesis aims to create a hybrid space that carefully calibrates information and architecture to initiate interactivity within home settings. The thesis adopts a human-centred design approach, including user interviews and iterative prototyping, to understand how augmented reality (AR) augments reality. Research focuses on young adults, primarily students and working professionals who have access to smart devices where increasing demand for personalized experiences present an ideal environment for AR to grow and thrive. Overall, the research and prototypes are representative of AR as a tool for novelty in familiarity, offering new ways of enhancing interactions and immersive experiences within the home. I believe this synthesis of our physical and digital elements will lead to a de-cluttered and productive mode of engagement

Bayesian Maps: probabilistic and hierarchical models for mobile robot navigation

What is a map? What is its utility? What is a location, a behaviour? What are navigation, localization and prediction for a mobile robot facing a given task ? These questions have neither unique nor straightforward answer to this day, and are still the core of numerous research domains. Robotics, for instance, aim at answering them for creating successful sensori-motor artefacts. Cognitive sciences use these questions as intermediate goals on the road to un- derstanding living beings, their skills, and furthermore, their intelligence. Our study lies between these two domains. We first study classical probabilistic ap- proaches (Markov localization, POMDPs, HMMs, etc.), then some biomimetic approaches (Berthoz, Franz, Kuipers). We analyze their respective advantages and drawbacks in light of a general formalism for robot programming based on bayesian inference (BRP). We propose a new probabilistic formalism for modelling the interaction between a robot and its environment : the Bayesian map. In this framework, defining a map is done by specifying a particular probability distri- bution. Some of the questions above then amount to solving inference problems. We define operators for putting maps together, so that " hierarchies of maps " and incremental development play a central role in our formalism, as in biomimetic approaches. By using the bayesian formalism, we also benefit both from a unified means of dealing with uncertainties, and from clear and rigorous mathematical foundations. Our formalism is illustrated by experiments that have been implemented on a Koala mobile robot