Real-time 3D SLAM for Humanoid Robot considering Pattern Generator Information

Abstract

Humanoid robotics and SLAM (simultaneous localisation and mapping) are certainly two of the most significant themes of the current worldwide robotics research effort, but the two fields have up until now largely run independent parallel paths, despite the obvious benefit to be gained in joining the two. The next major step forward in humanoid robotics will be increased autonomy, and the ability of a robot to create its own world map on the fly will be a significant enabling technology. Meanwhile, SLAM techniques have found most success with robot platforms and sensor configurations which are outside of the humanoid domain. Humanoid robots move with high linear and angular accelerations in full 3D, and normally only vision is available as an outward-looking sensor. Building on recently published work on monocular SLAM using vision, and on pattern generation, we show that real-time SLAM for a humanoid can indeed be achieved. Using HRP-2, we present results in which a sparse 3D map of visual landmarks is acquired on the fly using a single camera and demonstrated loop closing and drift-free 3D motion estimation within a typical cluttered indoor environment. This is achieved by tightly coupling the pattern generator, the robot odometry and inertial sensing to aid visual mapping within a standard EKF framework. To our knowledge this is the first implementation of real-time 3D SLAM for a humanoid robot able to demonstrate loop closing