论文标题
4季：一个跨季节数据集用于自动驾驶的多维大满贯
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
论文作者
论文摘要
我们提出了一个新颖的数据集，涵盖了自主驾驶的季节性和具有挑战性的知觉条件。除其他外，它还可以研究视觉景观测定，全球位置识别和基于地图的重新定位跟踪。数据是在不同的情况下以及在各种天气条件和照明下（包括白天和黑夜）收集的。这导致在九种不同的环境中产生了超过350公里的录音，从城市上的多层停车场（包括隧道）到乡村和高速公路。我们提供全球一致的参考姿势，并具有从直接立体视觉惯性循环仪与RTK-GNSS的融合中获得的最新精度。完整的数据集可在https://go.vision.in.tum.de/4S季节中找到。
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recognition, and map-based re-localization tracking. The data was collected in different scenarios and under a wide variety of weather conditions and illuminations, including day and night. This resulted in more than 350 km of recordings in nine different environments ranging from multi-level parking garage over urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up-to centimeter accuracy obtained from the fusion of direct stereo visual-inertial odometry with RTK-GNSS. The full dataset is available at https://go.vision.in.tum.de/4seasons.
