Fusion of 2d and 3d sensor data for articulated body tracking

Abstract

In this article, we present an approach for the fusion of 2d and 3d measurements for model-based person tracking, also known as Human Motion Capture. The applied body model is defined geometrically with generalized cylinders, and is set up hierarchically with connecting joints of different types. The joint model can be parameterized to control the degrees of freedom, adhesion and stiffness. This results in an articulated body model with constrained kinematic degrees of freedom. The fusion approach incorporates this model knowledge together with the measurements, and tracks the target body iteratively with an extended Iterative Closest Point (ICP) approach. Generally, the ICP is based on the concept of correspondences between measurements and model, which is normally exploited to incorporate 3d point cloud measurements. The concept has been generalized to represent and incorporate also 2d image space features. Together with the 3D point cloud from a 3d time-of-flight (ToF) camera, arbitrary features, derived from 2D camera images, are used in the fusion algorithm for tracking of the body. This gives complementary information about the tracked body, enabling not only tracking of depth motions but also turning movements of the human body, which is normally a hard problem for markerless human motion capture systems. The resulting tracking system, named VooDoo is used to track humans in a Human–Robot Interaction (HRI) context. We only rely on sensors on board the robot, i.e. the color camera, the ToF camera and a laser range finder. The system runs in realtime (∼20 Hz) and is able to robustly track a human in the vicinity of the robot.