发布首个面向头戴设备的视觉惯性跟踪数据集，解决真实场景下定位难题。
The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking
- 采集多款VR头显的真实运动序列，覆盖复杂挑战场景
- 包含高动态、低纹理、强光饱和等极端条件数据
- 开源免费，助力下一代视觉惯性定位系统研发
人形机器人和混合现实头显受益于头戴传感器进行追踪。尽管视觉惯性里程计（VIO）和同步定位与地图构建（SLAM）技术已取得显著进展，但现有系统在头戴设备典型应用场景中仍表现不佳。常见挑战如高强度运动、动态遮挡、长时间追踪、低纹理区域、恶劣光照、传感器饱和等，在现有文献数据集中覆盖不足，导致系统可能忽视这些关键现实问题。为此，我们提出Monado SLAM数据集，包含从多个虚拟现实头显采集的真实序列。该数据集以宽松的CC BY 4.0许可发布，旨在推动VIO/SLAM研究与开发的进步。
原文摘要 · Abstract (English)
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.
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