论文标题
在极其非结构化的环境中大满贯的挑战：DLR行星立体声，固态激光雷达，惯性数据集
Challenges of SLAM in extremely unstructured environments: the DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset
论文作者
论文摘要
我们介绍了DLR行星立体声，固态雷达，惯性（S3LI）数据集，记录在埃特纳山（Etna），西西里山（Sicily）上，一种类似于月球和火星的环境，使用了手持传感器套件，具有适用于在太空式移动杆上实现的属性。环境的特征是关于视觉和结构外观的具有挑战性的条件：严重的视觉混叠构成了视觉大满贯系统执行位置识别的能力的重大限制，而缺乏出色的结构细节，与使用点云估算的任务挑战了传统的Lidar Slam，挑战传统的LIDAR SLAM，仅使用Point Point Point Point Points clouds来挑战传统的Lidar Slam。借助这些数据，涵盖了在软火山斜坡上进行超过4公里的旅行，我们的目标是：1）提供一种工具，可以在环境中揭示最先进的大满贯系统的局限性，而这些环境中的环境并非广泛可用的数据集中以及2）2）激励新型定位和绘制方法的发展，这些方法有效地依赖于两种传感器的配置传感器。数据集可在以下URL：https：//rmc.dlr.de/s3li_dataset访问。
We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a hand-held sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-of-the-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors. The dataset is accessible at the following url: https://rmc.dlr.de/s3li_dataset
