The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking

TL;DR

The Monado SLAM dataset provides real-world VR headset sequences to improve visual-inertial tracking in challenging scenarios like occlusions and low light.

Contribution

It introduces a new dataset capturing challenging head-mounted tracking conditions to advance VIO/SLAM research.

Findings

Dataset includes sequences from multiple VR headsets.

Addresses issues like occlusions, lighting, and motion.

Aims to improve robustness of SLAM systems.

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.