The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking

• URL: http://arxiv.org/abs/2508.00088v1
• Date: Thu, 31 Jul 2025 18:28:07 GMT
• Title: The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking
• Authors: Mateo de Mayo, Daniel Cremers, Taihú Pire,
• Abstract summary: Humanoid robots and mixed reality headsets benefit from the use of head-mounted sensors for tracking.<n>We show that state-of-the-art tracking systems are still unable to gracefully handle many of the challenging settings presented in head-mounted use cases.<n>We present the Monado SLAM dataset, a set of real sequences taken from multiple virtual reality headsets.
• Score: 38.93284476165776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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