In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://go.vision.in.tum.de/4seasons.

在本文中，我们基于大规模 4Seasons 数据集，提出了一种新颖的视觉 SLAM 和长期定位基准，用于在具有挑战性的条件下进行自动驾驶。拟议的基准测试提供了由季节性变化以及不同的天气和照明条件引起的剧烈外观变化。虽然在具有类似条件的小规模数据集上推进视觉 SLAM 方面取得了重大进展，但仍然缺乏代表自动驾驶真实场景的统一基准。我们引入了一个新的统一基准，用于联合评估视觉里程计、全局位置识别和基于地图的视觉定位性能，这对于在任何条件下成功实现自动驾驶至关重要。这些数据已经收集了一年多，在九个不同的环境中进行了 300 多公里的记录，从多层停车场到城市（包括隧道）再到乡村和高速公路。我们提供全球一致的参考位姿，通过直接立体惯性里程计与 RTK GNSS 的融合获得高达厘米级的精度。我们在基准测试中评估了几种最先进的视觉里程计和视觉定位基线方法的性能，并分析了它们的特性。实验结果为当前方法提供了新的见解，并为未来的研究显示出有希望的潜力。我们的基准测试和评估协议将在 https://go.vision.in.tum.de/4seasons 上提供。