Economical Quaternion Extraction from a Human Skeletal Pose Estimate using 2-D Cameras
This work addresses the high cost and latency of traditional stereo cameras and IMUs for robotics researchers, making pose estimation more accessible for autonomous robot control systems.
The paper tackles the problem of extracting quaternions for human skeletal pose estimation from 2D camera frames, achieving sub-50 millisecond latency and enabling deployment on low-resource edge devices with a single camera.
In this paper, we present a novel algorithm to extract a quaternion from a two dimensional camera frame for estimating a contained human skeletal pose. The problem of pose estimation is usually tackled through the usage of stereo cameras and intertial measurement units for obtaining depth and euclidean distance for measurement of points in 3D space. However, the usage of these devices comes with a high signal processing latency as well as a significant monetary cost. By making use of MediaPipe, a framework for building perception pipelines for human pose estimation, the proposed algorithm extracts a quaternion from a 2-D frame capturing an image of a human object at a sub-fifty millisecond latency while also being capable of deployment at edges with a single camera frame and a generally low computational resource availability, especially for use cases involving last-minute detection and reaction by autonomous robots. The algorithm seeks to bypass the funding barrier and improve accessibility for robotics researchers involved in designing control systems.