  Visual Odometry (VO) is one of the fundamental tasks in computer vision for
robotics. However, its performance is deeply affected by High Dynamic Range
(HDR) scenes, omnipresent outdoor. While new Automatic-Exposure (AE) approaches
to mitigate this have appeared, their comparison in a reproducible manner is
problematic. This stems from the fact that the behavior of AE depends on the
environment, and it affects the image acquisition process. Consequently, AE has
traditionally only been benchmarked in an online manner, making the experiments
non-reproducible. To solve this, we propose a new methodology based on an
emulator that can generate images at any exposure time. It leverages BorealHDR,
a unique multi-exposure stereo dataset collected over 10 km, on 55 trajectories
with challenging illumination conditions. Moreover, it includes
lidar-inertial-based global maps with pose estimation for each image frame as
well as Global Navigation Satellite System (GNSS) data, for comparison. We show
that using these images acquired at different exposure times, we can emulate
realistic images, keeping a Root-Mean-Square Error (RMSE) below 1.78 % compared
to ground truth images. To demonstrate the practicality of our approach for
offline benchmarking, we compared three state-of-the-art AE algorithms on key
elements of Visual Simultaneous Localization And Mapping (VSLAM) pipeline,
against four baselines. Consequently, reproducible evaluation of AE is now
possible, speeding up the development of future approaches. Our code and
dataset are available online at this link:
https://github.com/norlab-ulaval/BorealHDR
