  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.
