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.

本文提出了一种快速、高效、准确的人体和相机状态估计框架 BodySLAM++，通过视觉惯性数据对人体状态和相机状态同时进行估计，并在 Intel i7 处理器的环境下实现了 15 + 帧每秒的实时性能，相对于基准方法，我们的系统分别提高了 26% 和 12% 的人体和相机状态估计准确率。