Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 67K undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e. loop closure detection) and inter-sequence (i.e. re-localisation) place recognition. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code will be available at https://csiro-robotics.github.io/Wild-Places.

翻译：为解决这一问题,我们引入了Ward-Places,这是一个具有挑战性的大型数据集,用于在没有结构的自然环境中确认Lidar地点。Ward-Places包含14个月中用手持传感器有效载荷收集的8个Lidar序列,其中共包含67K未变的Lidar 子图象以及准确的 6DoF 地面真象。我们的数据集包含在序列内和序列间进行的多次重访,允许在序列内(即循环闭合检测)和序列间(即重新定位)地点识别。我们还以若干最先进的状态方法为基准,以展示该数据集在14个月内产生的挑战,特别是由于自然环境随时间变化而长期识别的位置的例子。我们的数据设置和代码将在 https://Prgibrotics.s.s.slace/svidrl.s.s/sirbbrostal.s.s.