Image hashing for loop closing in underwater visual SLAM

This article presents an experimental assessment of a hash-based loop closure detection methodology specially addressed to Multi-robot underwater visual Simultaneous Localization and Mapping (SLAM). This methodology uses two diferent top quality image global descriptors, one learned (NetVLAD) and one handcrafted (HALOC). Complete tests were done to compare the performance of both hashing techniques applied in an extensive set of real underwater imagery.Peer Reviewe

Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

This paper presents our approach to develop a method for an unmanned ground vehicle (UGV) to perform inspection tasks in nuclear environments using rich information maps. To reduce inspectors' exposure to elevated radiation levels, an autonomous navigation framework for the UGV has been developed to perform routine inspections such as counting containers, recording their ID tags and performing gamma measurements on some of them. In order to achieve autonomy, a rich information map is generated which includes not only the 2D global cost map consisting of obstacle locations for path planning, but also the location and orientation information for the objects of interest from the inspector's perspective. The UGV's autonomy framework utilizes this information to prioritize locations to navigate to perform the inspections. In this paper, we present our method of generating this rich information map, originally developed to meet the requirements of the International Atomic Energy Agency (IAEA) Robotics Challenge. We demonstrate the performance of our method in a simulated testbed environment containing uranium hexafluoride (UF6) storage container mock ups

Multi-Session Visual SLAM for Illumination Invariant Localization in Indoor Environments

For robots navigating using only a camera, illumination changes in indoor environments can cause localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, FREAK, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minutes intervals during sunset using a Google Tango phone in a real apartment.Comment: 6 pages, 5 figure

Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas

This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Estudo de algoritmos SLAM para navegação autónoma em ambientes não estruturados

Robotics is one of the most exciting areas that has been through constant innovation and evolution over the years. Robots have become more and more a part of our lives and are no longer a vision for the future but a reality of the present. Nowadays we have robots cleaning our home, vacuuming our floors, playing soccer or even exploring the unknown outside of our planet. Robots are a major theme in research projects with special attention given to mobile robots since they have the capability to navigate the environment and interact more easily with humans. In the last couple of years, we have observed a big growth in the market of service robots. A service robot is dedicated to help humans in their everyday tasks. While reactive or pre-programmed behaviors are sufficient to let a robot appear intelligent, to be truly be intelligent a robot must learn and adapt to its environment. SLAM is the computational problem of learning an environment by constructing its map while simultaneously keeping track of the robot location inside it. Follow Inspiration is a company focused on the development of robotic systems. The autonomous shopping cart WiiGo was its first product, it is an autonomous service robot designed to help people carry their purchases in supermarkets. In this document we describe the testing and integration of SLAM algorithms, development of a marker-based solution to detect interest-points and the development of visualization tools for the WiiGo robot. The results presented in this document allowed the WiiGo robot to become capable of autonomous navigation in human occupied environments independently, without resourcing to external localization systems.A robótica é uma das áreas mas excitantes e dinâmicas que tem apresentado um elevado crescimento ao longo dos últimos anos. Robôs tornaram-se parte da nossa vida e não são mais uma visão do futuro. Atualmente temos robôs a limpar as nossas casas, aspirar o chão, jogar futebol e até a explorar o desconhecido fora do nosso planeta. A robótica é um dos maiores temas de investigação atualmente com especial foco em robôs móveis. Estes robôs são capazes de se movimentar e interagir com o ambiente abrindo caminho para novas possibilidades possibilitando novas formas de interação com humanos. Nos últimos anos foi possível observar um grande crescimento do mercado de robôs de serviço. Estes robôs têm como objetivo auxiliar humanos na execução de tarefas diárias. Comportamentos reativos ou pré programados são suficientes para fazer um robô parecer inteligente, mas para um robô ser realmente inteligente deve aprender e adaptar-se ao seu ambiente. SLAM é o problema computacional de aprender um ambiente criando um mapa do mesmo enquanto simultaneamente se estima a localização do robô dentro do ambiente aprendido. A Follow Inspiration é uma empresa focada no desenvolvimento e produção de sistemas robóticos. O robô WiiGo foi o primeiro produto da empresa, é um robô de serviço que tem como objectivo auxiliar clientes de supermercados a carregar as suas compras. Neste documento apresentamos testes e integração de algoritmos de SLAM, desenvolvimento de um sistema baseado em marcadores visuais para detecção de pontos de interesse e desenvolvimento de ferramentas de visualização de dados para o robô WiiGo. Os resultados apresentado possibilitaram que o robô WiiGo se torna-se capaz de navegação autónoma em ambientes ocupados por humanos sem recurso a sistemas de localização externos.Mestrado em Engenharia de Computadores e Telemátic