标题： 在野外对相机自动曝光方法的可重复评估：平台、基准和经验教训 显示英文标题

标题： Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned

摘要： 标准数据集通常存在局限性，特别是由于输入数据传感器的固定性质，这使得难以比较那些主动调整传感器参数以适应环境条件的方法。 这是自动曝光（AE）方法的情况，这些方法依赖于环境因素来影响图像采集过程。 因此，AE方法传统上是以在线方式进行基准测试的，导致实验不可复现。 在我们之前工作的基础上，我们提出了一种方法，该方法利用能够生成任何曝光时间图像的模拟器。 这种方法利用了BorealHDR，这是一个独特的多曝光立体数据集，以及其新的扩展部分，在不同时间段沿重复轨迹获取数据，以评估光照变化的影响。 总共，BorealHDR覆盖了59条轨迹中的13.4公里，在具有挑战性的光照条件下。 该数据集还包括基于激光雷达-惯性里程计的地图，以及每帧图像的姿态估计，以及用于比较的全球导航卫星系统（GNSS）数据。 我们证明，通过使用不同曝光时间获取的图像，我们可以生成与真实图像相比均方根误差（RMSE）低于1.78%的真实图像。 使用这种离线方法，我们对八种AE方法进行了基准测试，得出结论认为经典AE方法仍然是该领域的最佳表现者。 为了进一步支持可复现性，我们提供了背包采集平台开发的详细信息，包括硬件、电气部件和性能规格。 此外，我们分享了在超过25公里的各种环境中部署背包所获得的宝贵经验。 我们的代码和数据集可在以下链接在线获取：https://github.com/norlab-ulaval/TFR24 BorealHDR 显示英文摘要

摘要： 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