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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Learning Weather Dependent Image Features for Robust Localization |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | Image Retrieval based Localization is a promising vision-based solution due to its robustness and efficiency. Retrieval-based localization matches any environment query image to a set of database images with known geo-tagged location information. Learned image feature embedding is generally used to match query and database images. Through literature study we found condition invariant features are used for image retrieval whereas we propose a method to learn weather condition dependent image features. We show that different weather conditions prefer different pooling layers. Conditional Neural Architecture Search (CNAS) can be used to select to select and learn suitable pooling layers for different query image weather conditions. Our experimental results on the Oxford Robotcar dataset and CMU-Seasons show the superiority of our approach and justify the use of different pooling layers based on weather conditions. We are able to improve the mean retrieval accuracy compared to the previous state-of-the-art on the Oxford Dataset from 69.4% to 76.2% points with the night-rain weather condition improvement from 32.9% to 61.3% at a 5m tolerance limit. Further experiments on CIFAR-10 and MNIST datasets, shows the effectiveness of CNAS on classification tasks. |
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