Abstract
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.
Abstract (translated)
人形机器人和混合现实头戴设备从使用头部安装传感器进行追踪中受益匪浅。尽管视觉惯性里程计（VIO）和即时定位与地图构建（SLAM）技术的进步产生了新的高质量最先进的追踪系统，但我们发现这些系统仍然无法优雅地应对许多出现在头部佩戴式应用中的挑战场景。常见的场景如高强度动作、动态遮挡、长时间的跟踪会话、低纹理区域、恶劣光照条件以及传感器饱和等问题，在现有文献中的数据集中仍被覆盖不足。因此，可能无意中忽略了这些问题在现实世界中的重要性。 为了解决这一问题，我们提出了Monado SLAM数据集，这是一个从多个虚拟现实头戴设备采集的真实序列集合。我们将该数据集以宽松的CC BY 4.0许可协议发布，旨在推动VIO/SLAM研究和开发领域的进步。
URL
https://arxiv.org/abs/2508.00088
