The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking

2025-07-31

AI Summary

Existing VIO/SLAM datasets lack systematic coverage of key challenges inherent to real-world head-mounted device usage—such as high-frequency head motion, dynamic occlusions, low-texture environments, abrupt illumination changes, sensor saturation, and long-term drift—thereby hindering robustness improvements. To address this, we introduce the first large-scale, open-source dataset specifically designed for head-mounted visual-inertial tracking. It comprises synchronized RGB images and IMU measurements from multiple VR headsets operating in diverse, complex environments, featuring precise spatiotemporal alignment, comprehensive modeling of realistic disturbances, and continuous sequences lasting several minutes. Crucially, it is the first dataset to systematically integrate high-dynamic motion, dynamic occlusions, and sensor saturation—thus filling a critical gap in long-duration, high-interference VIO/SLAM evaluation benchmarks. Released under the CC BY 4.0 license, it significantly advances the generalization capability and real-world deployment reliability of localization and mapping algorithms in embodied AI and mixed reality.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Addresses poor handling of high-intensity motions in head-mounted tracking
• Improves performance in low-textured and adverse lighting conditions
• Provides dataset for long tracking sessions and dynamic occlusions

Innovation

• Introduces Monado SLAM dataset for VIO/SLAM
• Captures real sequences from VR headsets
• Addresses challenging head-mounted tracking scenarios