  We propose a multi-sensor fusion method for capturing challenging 3D human
motions with accurate consecutive local poses and global trajectories in
large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up
conveniently and worn lightly. Specifically, to fully utilize the global
geometry information captured by LiDAR and local dynamic motions captured by
IMUs, we design a two-stage pose estimator in a coarse-to-fine manner, where
point clouds provide the coarse body shape and IMU measurements optimize the
local actions. Furthermore, considering the translation deviation caused by the
view-dependent partial point cloud, we propose a pose-guided translation
corrector. It predicts the offset between captured points and the real root
locations, which makes the consecutive movements and trajectories more precise
and natural. Moreover, we collect a LiDAR-IMU multi-modal mocap dataset, LIPD,
with diverse human actions in long-range scenarios. Extensive quantitative and
qualitative experiments on LIPD and other open datasets all demonstrate the
capability of our approach for compelling motion capture in large-scale
scenarios, which outperforms other methods by an obvious margin. We will
release our code and captured dataset to stimulate future research.
