BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking
This work addresses real-time human and camera state estimation for applications like robotics or AR, but it is incremental as it extends an existing framework (OKVIS2).
The paper tackles the problem of robust, fast, and accurate human state (6D pose and posture) estimation by presenting BodySLAM++, a visual-inertial framework that simultaneously estimates camera and human states, improving accuracy by 26% for human and 12% for camera state estimation and achieving real-time performance at 15+ fps on an Intel i7 CPU.
Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in real-time is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state estimation framework relying on visual-inertial data. BodySLAM++ extends an existing visual-inertial state estimation framework, OKVIS2, to solve the dual task of estimating camera and human states simultaneously. Our system improves the accuracy of both human and camera state estimation with respect to baseline methods by 26% and 12%, respectively, and achieves real-time performance at 15+ frames per second on an Intel i7-model CPU. Experiments were conducted on a custom dataset containing both ground truth human and camera poses collected with an indoor motion tracking system.