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

TLDR

Humanoid robotics and SLAM are major research themes that have largely progressed independently, yet integrating them is essential for the next step toward autonomous robots that can build their own maps on the fly. This work aims to show that real‑time SLAM for a humanoid can be achieved by combining monocular vision with pattern generation. The system tightly couples the pattern generator, robot odometry, and inertial sensing within an EKF framework to support visual mapping. On the HRP‑2, we achieved on‑the‑fly sparse 3D mapping with a single camera, demonstrating loop closing, drift‑free motion estimation in cluttered indoor environments, and marking the first real‑time 3D SLAM implementation for a humanoid.

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