Active Exposure Control for Robust Visual Odometry in High Dynamic Range (HDR) Environments

In this paper, we propose an active exposure control method to improve the robustness of visual odometry in HDR (high dynamic range) environments. Our method evaluates the proper exposure time by maximizing a robust gradient-based image quality metric. The optimization is achieved by exploiting the photometric response function of the camera. Our exposure control method is evaluated in different real-world environments and outperforms the camera's built-in auto-exposure function and a fixed exposure time. To validate the benefit of our approach, we test different state-of-the-art visual odometry pipelines (namely, ORB-SLAM2, DSO, and SVO 2.0) with our active exposure control algorithm and demonstrate significantly improved performance using our exposure control method in very challenging HDR environments! Datasets and code will be released soon!
Reference:
Z. Zhang, C. Forster, D. Scaramuzza
Active Exposure Control for Robust Visual Odometry in HDR Environments
IEEE International Conference on Robotics and Automation (ICRA), 2017.
PDF: rpg.ifi.uzh.ch/...
Code: github.com/uzh...
Our research page on visual odometry:
rpg.ifi.uzh.ch/...
Robotics and Perception Group, University of Zurich, 2017
rpg.ifi.uzh.ch/

[@dharmakc8944]

Could you please provide the link to the github. Thank you.

[@chuanbao8946]

are you found this github? thank you

[@Jasonreninsh]

Could u share the dataset about this paper to us? Tks in advance.