The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking
MCML Authors
Daniel Cremers
Prof. Dr.
Director
Abstract
Daniel Cremers
Prof. Dr.
Director
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.
inproceedings MCP25
IROS 2025
IEEE/RSJ International Conference on Intelligent Robots and Systems. Hangzhou, China, Oct 19-25, 2025.
Authors
M. de Mayo • D. Cremers • T. PireLinks
DOIResearch Area
BibTeXKey: MCP25