Unsupervised learning of terrain representations for haptic Monte Carlo localization
Haptic sensing has recently been used effectively for legged robot localization in extreme scenarios where cam-eras and LiDAR might fail, such as dusty mines and foggy sewers. However, existing haptic sensing mainly relies on supervised classification, with training and evaluation executed over expl...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2022
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