标题： Monado SLAM数据集用于第一视角视觉惯性跟踪 显示英文标题

标题： The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking

摘要： 人形机器人和混合现实头戴设备受益于使用头戴式传感器进行跟踪。 尽管视觉惯性里程计（VIO）和同时定位与地图构建（SLAM）的进步已经产生了新的高质量最先进的跟踪系统，但我们表明这些系统仍然无法优雅地处理头戴式使用案例中提出的许多挑战性环境。 常见的场景如高强度运动、动态遮挡、长时间跟踪会话、低纹理区域、恶劣的光照条件、传感器饱和等，现有文献中的数据集在这些方面仍然覆盖不足。 在这种情况下，系统可能会无意中忽略这些关键的真实世界问题。 为了解决这个问题，我们提出了Monado SLAM数据集，这是一组从多个虚拟现实头戴设备中获取的真实序列。 我们以宽松的CC BY 4.0许可证发布该数据集，以推动VIO/SLAM研究和开发的进步。 显示英文摘要

摘要： Humanoid robots and mixed reality headsets benefit from the use of head-mounted sensors for tracking. While advancements in visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM) have produced new and high-quality state-of-the-art tracking systems, we show that these are still unable to gracefully handle many of the challenging settings presented in the head-mounted use cases. Common scenarios like high-intensity motions, dynamic occlusions, long tracking sessions, low-textured areas, adverse lighting conditions, saturation of sensors, to name a few, continue to be covered poorly by existing datasets in the literature. In this way, systems may inadvertently overlook these essential real-world issues. To address this, we present the Monado SLAM dataset, a set of real sequences taken from multiple virtual reality headsets. We release the dataset under a permissive CC BY 4.0 license, to drive advancements in VIO/SLAM research and development.