Real-time human body motion estimation based on multi-layer laser scans

Real time human body motion estimation plays an important role in the perception for robotics nowadays, especially for the applications of human robot interaction and service robotics. In this paper, we propose a method for real-time 3D human body motion estimation based on 3-layer laser scans. All the useful scanned points, presenting the human body contour information, are subtracted from the learned background of the environment. For human contour feature extraction, in order to avoid the situations of unsuccessful segmentation, we propose a novel iterative template matching algorithm for clustering, where the templates of torso and hip sections are modeled with different radii. Robust distinct human motion features are extracted using maximum likelihood estimation and nearest neighbor clustering method. Subsequently, the positions of human joints in 3D space are retrieved by associating the extracted features with a pre-defined articulated model of human body. Finally we demonstrate our proposed methods through experiments, which show accurate human body motion tracking in real time.