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://www.4seasons-dataset.com.
翻译：我们展示了一套新颖的数据集,涵盖自主驾驶的季节性和具有挑战性的概念性条件,除其他外,它能够进行视觉视像测量、全球地点识别和基于地图的重新定位跟踪等研究,这些数据是在不同的情景下,在包括白天和夜间在内的各种天气条件和照明下收集的,在从城市(包括隧道)到农村和高速公路的多层停车场到农村和高速公路的9个不同环境中,共录制了350多公里的录音。我们提供了全球一致的参照,从立体声直观内脏测量与RTK-GNS的直接立体视觉内脏测量结合中获得了直径至厘米的精确度。完整的数据集可在https://www.4sesons-dataset.com上查阅。
相关内容
微信扫码咨询专知VIP会员