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Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

2021-02-08 16:46:09
Justin Tomasi, Brandon Wagstaff, Steven L. Waslander, Jonathan Kelly

Abstract

Successful visual navigation depends upon capturing images that contain sufficient useful information. In this paper, 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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.04341

PDF

https://arxiv.org/pdf/2102.04341.pdf


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