Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910916954554368 |
|---|---|
| author | Milford, Michael Turner, Ian Corke, Peter |
| author_facet | Milford, Michael Turner, Ian Corke, Peter |
| contents | In this paper we evaluate performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments with low-cost cameras that result in massively blurred images. We evaluate the effect of motion blur from exposure times up to 10,000 ms from a moving car, and the performance of localization in day time from routes learned at night in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed grayscale images to using patch normalization and local neighborhood normalization - the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function despite extreme appearance change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long Exposure Localization in Darkness Using Consumer Cameras Milford, Michael Turner, Ian Corke, Peter Robotics In this paper we evaluate performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments with low-cost cameras that result in massively blurred images. We evaluate the effect of motion blur from exposure times up to 10,000 ms from a moving car, and the performance of localization in day time from routes learned at night in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed grayscale images to using patch normalization and local neighborhood normalization - the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function despite extreme appearance change. |
| title | Long Exposure Localization in Darkness Using Consumer Cameras |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.16406 |