Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
| Autor: | Tomasi, Justin, Wagstaff, Brandon, Waslander, Steven L., Kelly, Jonathan |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Robotics and Automation Letters (RA-L), Vol. 6, No. 2, pp. 2028-2035, Apr. 2021 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/LRA.2021.3058909 |
| Popis: | Successful visual navigation depends upon capturing images that contain sufficient useful information. In this letter, we explore a data-driven approach to account for environmental lighting changes, improving the quality of images for use in visual odometry (VO) or visual simultaneous localization and mapping (SLAM). We train a deep convolutional neural network model to predictively adjust camera gain and exposure time parameters such that consecutive images contain a maximal number of matchable features. The training process is fully self-supervised: our training signal is derived from an underlying VO or SLAM pipeline and, as a result, the model is optimized to perform well with that specific pipeline. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes (e.g., transitions into and out of road tunnels), maintaining a substantially higher number of inlier feature matches than competing camera parameter control algorithms. Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'21), Xi'an, China, May 30-Jun. 5, 2021 |
| Databáze: | arXiv |
| Externí odkaz: |
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