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

Semantic Augmented Reality Environment with Material-Aware Physical Interactions

© 2017 IEEE. In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience. Much of recent advances in AR has been largely focused on developing geometry-aware environment, but little has been done in dealing with interactions at the semantic level. High-level scene understanding and semantic descriptions in AR would allow effective design of complex applications and enhanced user experience. In this paper, we present a novel approach and a prototype system that enables the deeper understanding of semantic properties of the real world environment, so that realistic physical interactions between the real and the virtual objects can be generated. A material-aware AR environment has been created based on the deep material learning using a fully convolutional network (FCN). The state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) has been used for the semantic mapping. Together with efficient accelerated 3D ray casting, natural and realistic physical interactions are generated for interactive AR games. Our approach has significant impact on the future development of advanced AR systems and applications

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

A Platform for Indoor Localisation, Mapping, and Data Collection using an Autonomous Vehicle

Everyone who has worked with research knows how rewarding experimenting and developing new algorithms can be. However in some cases, the hard part is not the invention of these algorithms, but their evaluation. To try and make that evaluation easier, this thesis focuses on the collection of data that can be used as positional ground truths using an autonomous measurement platform. This should assist Combain Mobile AB in the evaluation and improvement of their Wi-Fi based indoor positioning service. How and which parts of the open-source community’s work in the Robot Operating System (ROS) project to utilise is not obvious. This thesis therefore sets out to build a Minimum Viable Product (MVP) which is capable of supporting two different use cases: measure and explore inside an unknown environment, and measure inside a known environment given a map. This effectively leaves Combain with a viable product, and indirectly helps the community by aiding it in comparing and recommending the best tools and software libraries for the task. The result of this thesis ends up recommending the following for measuring inside an unknown environment: the Simultaneous Localisation And Mapping (SLAM) algorithm Google Cartographer for navigation, and the exploration algorithm Hector Exploration for planning the exploration. To measure inside a known environment the following is recommended: the Adaptive Monte Carlo Localisation (AMCL) positioning algorithm and the Spanning Tree Covering algorithm.Data har många användningsområden inom både forskning och industri. I detta examensarbete skapades en platform som självgående kan användas för att samla in stora mängder data från omgivningen

Autonomous mobility scooters as assistive tools for the elderly

The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path