We introduce RoboPose, a method to estimate the joint angles and the 6D
camera-to-robot pose of a known articulated robot from a single RGB image. This
is an important problem to grant mobile and itinerant autonomous systems the
ability to interact with other robots using only visual information in
non-instrumented environments, especially in the context of collaborative
robotics. It is also challenging because robots have many degrees of freedom
and an infinite space of possible configurations that often result in
self-occlusions and depth ambiguities when imaged by a single camera. The
contributions of this work are three-fold. First, we introduce a new render &
compare approach for estimating the 6D pose and joint angles of an articulated
robot that can be trained from synthetic data, generalizes to new unseen robot
configurations at test time, and can be applied to a variety of robots. Second,
we experimentally demonstrate the importance of the robot parametrization for
the iterative pose updates and design a parametrization strategy that is
independent of the robot structure. Finally, we show experimental results on
existing benchmark datasets for four different robots and demonstrate that our
method significantly outperforms the state of the art. Code and pre-trained
models are available on the project webpage
this https URL

RoboPose 是一种从单个 RGB 图像估计已知关节机器人的关节角度和 6D 相机到机器人姿态的方法，可用于协作机器人，并使用合成数据进行训练，性能超过现有的技术。