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- W2912287943 abstract "In order for a robot to intelligently interact in real-world environments, it is essential to understand its surrounding. The surrounding can be represented as a metric map. However, most of the current mapping techniques assume the environment is static, and therefore, the control policies cannot effectively account for the changing and unpredictable nature of the real world environments. Therefore, it is essential to model the uncertainties in dynamic environments.In this thesis, three types of mapping techniques to characterize patterns that change in both space and time are proposed. Firstly, long-term occupancy maps to model variations of the occupancy state of the environment observed over a period is proposed. Such maps are developed with the intention of providing prior information for path planning algorithms. Secondly, short-term occupancy maps to model the immediate changes in the occupancy level are developed. The short-term maps are useful for safe real-time navigation in dynamic environments by making predictions into the future. Finally, directional grid maps are introduced to model the angular uncertainty inherent in robotics.In order to captures the highly nonlinear spatiotemporal patterns in the real-world environments, kernel methods that operate in a rich high-dimensional feature space is utilized. In order to quantify the uncertainty in dynamic environments, Bayesian inference and directional statistics are mainly used. The proposed methods are suitable for both big data and data-scarce settings. Experiments were conducted on simulated and real-world robotic systems, mainly using data obtained from range sensors such as LIDAR. Besides having attractive theoretical properties, the proposed learning algorithms are superior in both speed and accuracy. Further, because the proposed techniques in this thesis can effectively model the uncertainty in changing environments, they can be used for exploration and path planning in dynamic environments." @default.
- W2912287943 created "2019-02-21" @default.
- W2912287943 creator A5043482412 @default.
- W2912287943 date "2018-08-31" @default.
- W2912287943 modified "2023-09-28" @default.
- W2912287943 title "Nonlinear Methods for Spatiotemporal Mapping in Unstructured Environments" @default.
- W2912287943 hasPublicationYear "2018" @default.
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