VOCA: Visual Odometry with Codec Awareness

Camera pose estimation from image streams is a critical component of spatial world models that integrate perception into planning and decision-making. Nearly all Visual Odometry (VO) and Simultaneous Localization and Mapping (V-SLAM) systems have focused on datasets containing raw, uncompressed videos. Many working systems instead use ubiquitous hardware units to efficiently compress and decode video streams, saving orders of magnitude in storage and bandwidth. However, this lossy compression introduces visual artifacts that hinder the performance of traditional tracking systems. We present VOCA, a causal stereo visual-odometry method that exploits codec information to improve tracking performance. We achieve state-of-the-art performance on causal VO for relative trajectory error, efficiency, and absolute trajectory error on compressed streams. This work highlights the potential of leveraging widely available video codec information for vision tasks.

The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking

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.