O. Stasse, Andrew Davison, R. Sellaouti, K. Yokoi
Humanoid robotics and SLAM (Simultaneous Lo-
calisation 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.
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