Sim-to-Real RNN-Based Framework for the Precise Positioning of Autonomous Mobile Robots
This work proposes a recurrent neural network-based sim-to-real method to learn mobile robot localization using lidar data in dynamic environments. The main aim of the algorithm is to estimate a Cartesian position error relative to a saved position by means of stored lidar readings in a two-dimensio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10738792/ |
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