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- W4317242653 abstract "Autonomous exploration is the prerequisite for reli-able robot navigation in unknown environments via providing the necessary information. The goal is to build a precise environment map while at the same time locating the robot robustly. To achieve this goal, this paper develops an information map that jointly provides the information of mapping and localization uncertainty. It consists of a localization uncertainty map and a model uncertainty map. The former evaluates the landmarks around the robot and the latter indicates the map completeness based on information theory. The information map serves as the input for making better decisions of where-to-go to achieve higher localization and mapping performance. To improve the efficiency of information map prediction, we develop a learning-based map prediction framework, where a dataset is established autonomously. The effectiveness and efficiency of the developed framework are verified through experiments in simulation environments." @default.
- W4317242653 created "2023-01-18" @default.
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- W4317242653 date "2022-12-05" @default.
- W4317242653 modified "2023-09-28" @default.
- W4317242653 title "Information Map Prediction based on Learning Network for Reinforced Autonomous Exploration" @default.
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- W4317242653 doi "https://doi.org/10.1109/robio55434.2022.10011800" @default.
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