763 research outputs found

    Multi-Support Gaussian Processes for Continuous Occupancy Mapping

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    Robotic mapping enables an autonomous agent to build a representation of its environment based upon sensorial information. In particular, occupancy mapping aims at classifying regions of space according to whether or not they are occupied---and, therefore, inaccessible to the agent. Traditional techniques rely on discretisation to perform this task. The problems tackled by this thesis stem from the discretisation of continuous phenomena and from the inherently inaccurate and large datasets typically created by state-of-the-art robotic sensors. To approach this challenge, we make use of statistical modelling to handle the noise in the data and create continuous occupancy maps. The proposed approach makes use of Gaussian processes, a non-parametric Bayesian inference framework that uses kernels, to handle sensor noise and learn the dependencies among data points. The main drawback is the method's computational complexity, which grows cubically with the number of input points. The contributions of this work are twofold. First, we generalise kernels to be able to handle inputs in the form of areas, as well as points. This allows groups of spatially correlated data points to be condensed into a single entry, considerably reducing the size of the covariance matrix and enabling the method to deal efficiently with large amounts of data. Then, we create a mapping algorithm that makes use of Gaussian processes equipped with this kernel to build continuous occupancy maps. Experiments were conducted, using both synthetic and publicly available real data, to compare the presented algorithm with a similar previous method. They show it to be comparably accurate, yet considerably faster when dealing with large datasets

    Dataset of Panoramic Images for People Tracking in Service Robotics

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    We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility.We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility

    Towards Palm-Size Autonomous Helicopters

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    muFly EU project started in 2006 with the idea to build an autonomous micro helicopter, comparable in size and weight to a small bird. Several scientific and technological objectives were identified. This spanned from system-level integration, high efficiency micro-actuation, highly integrated micro vision sensors and IMUs and also low processing power navigation algorithms. This paper shows how most of these objectives were reached, describing the approach and the role of each partner during the whole project. The paper describes also the technological developments achieved like the 80g, 17 cm micro robotic-helicopter, the 8g omnidirectional and steady-state laser scanner, the uIMU, the highly efficient micro motors, the high power-density fuel-cell and the successful graph-based navigation algorithm
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