提出可复现的相机自动曝光评估方法，解决传统在线测试不可重现问题。
Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned
- 用模拟器生成任意曝光时间图像，实现离线评估
- 在13.4公里、59条轨迹上验证，误差低于1.78%RMSE
- 首次提供可复现的硬件平台与部署经验，适合自动驾驶研究者
标准数据集常因输入数据传感器固定，难以评估主动调节传感器参数的自动曝光（AE）方法。传统在线评测方式导致实验不可复现。本文基于先前工作，提出一种利用模拟器生成任意曝光时间图像的方法，结合BorealHDR这一独特的多曝光立体数据集及其新扩展——在不同时间段沿重复轨迹采集的数据，用于评估光照变化影响。该数据集覆盖13.4公里、59条轨迹，涵盖复杂光照条件，并包含基于激光雷达-惯性-里程计的位姿估计及GNSS数据。实验表明，通过不同曝光时间图像重建，可实现与真实图像的均方根误差低于1.78%。采用此离线方法，我们对八种AE方法进行了基准测试，结果表明经典方法仍是当前最佳。为保障可复现性，本文详细公开了背包式采集平台的硬件、电气组件及性能规格，并分享超过25公里跨环境部署的经验。代码与数据已开源：https://github.com/norlab-ulaval/TFR24 BorealHDR。
原文摘要 · Abstract (English)
Standard datasets often present limitations, particularly due to the fixed nature of input data sensors, which makes it difficult to compare methods that actively adjust sensor parameters to suit environmental conditions. This is the case with Automatic-Exposure (AE) methods, which rely on environmental factors to influence the image acquisition process. As a result, AE methods have traditionally been benchmarked in an online manner, rendering experiments non-reproducible. Building on our prior work, we propose a methodology that utilizes an emulator capable of generating images at any exposure time. This approach leverages BorealHDR, a unique multi-exposure stereo dataset, along with its new extension, in which data was acquired along a repeated trajectory at different times of the day to assess the impact of changing illumination. In total, BorealHDR covers 13.4 km over 59 trajectories in challenging lighting conditions. The dataset also includes lidar-inertial-odometry-based maps with pose estimation for each image frame, as well as Global Navigation Satellite System (GNSS) data for comparison. We demonstrate that by using images acquired at various exposure times, we can emulate realistic images with a Root-Mean-Square Error (RMSE) below 1.78% compared to ground truth images. Using this offline approach, we benchmarked eight AE methods, concluding that the classical AE method remains the field's best performer. To further support reproducibility, we provide in-depth details on the development of our backpack acquisition platform, including hardware, electrical components, and performance specifications. Additionally, we share valuable lessons learned from deploying the backpack over more than 25 km across various environments. Our code and dataset are available online at this link: https://github.com/norlab-ulaval/TFR24 BorealHDR
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