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

• URL: http://arxiv.org/abs/2102.04341v1
• Date: Mon, 8 Feb 2021 16:46:09 GMT
• Title: Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
• Authors: Justin Tomasi, Brandon Wagstaff, Steven L. Waslander, Jonathan Kelly
• Abstract summary: 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. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes.
• Score: 12.870196901446208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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