Proactive Camera Attribute Control Using Bayesian Optimization for Illumination-Resilient Visual Navigation

Joowan Kim, Younggun Cho, Ayoung Kim

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Illumination variance is a major challenge for vision-based robotics. Most approaches focus on alleviating illumination changes in already captured images. Despite the large utility, camera attributes have been empirically determined to function in a highly passive manner, yielding vision algorithm failure under radical illumination variance. Recent studies have proposed exposure and gain control schemes that could maximize image information and eschew saturation. In this article, we propose a proactive control scheme for the camera's two dominant attributes-exposure time and gain control. Unlike existing approaches, we formulate this camera attribute control as an optimization problem in which the underlying function is not known a priori. We first define a new metric of the image regarding these two major attributes to include both image gradients and signal-to-noise ratio simultaneously. Based on this metric, we introduce a new formulation for this attribute control via Bayesian optimization (BO) and learn the environmental change from the captured image. During the control, to mitigate the burden of image acquisition and Bayesian optimization, images are synthesized using a camera response function and avoided the actual frame grab from the camera. The proposed method was validated in light-flickering indoor, outdoor near sunset, and indoor-outdoor transient environments where light changes rapidly, supporting 20-40 Hz frame rates.

Original languageEnglish
Article number9098963
Pages (from-to)1256-1271
Number of pages16
JournalIEEE Transactions on Robotics
Volume36
Issue number4
DOIs
StatePublished - Aug 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • Camera attribute control
  • computer vision for other robotic application
  • field robots
  • visual-based navigation

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