W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911905041350656 |
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| author | Schreiber, Andre Sivakumar, Arun N. Du, Peter Gasparino, Mateus V. Chowdhary, Girish Driggs-Campbell, Katherine |
| author_facet | Schreiber, Andre Sivakumar, Arun N. Du, Peter Gasparino, Mateus V. Chowdhary, Girish Driggs-Campbell, Katherine |
| contents | Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation--which predicts where in the environment a robot can travel--is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02822 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics Schreiber, Andre Sivakumar, Arun N. Du, Peter Gasparino, Mateus V. Chowdhary, Girish Driggs-Campbell, Katherine Robotics Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation--which predicts where in the environment a robot can travel--is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments. |
| title | W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.02822 |