  We present an approach for estimating a mobile robot's pose w.r.t. the
allocentric coordinates of a network of static cameras using multi-view RGB
images. The images are processed online, locally on smart edge sensors by deep
neural networks to detect the robot and estimate 2D keypoints defined at
distinctive positions of the 3D robot model. Robot keypoint detections are
synchronized and fused on a central backend, where the robot's pose is
estimated via multi-view minimization of reprojection errors. Through the pose
estimation from external cameras, the robot's localization can be initialized
in an allocentric map from a completely unknown state (kidnapped robot problem)
and robustly tracked over time. We conduct a series of experiments evaluating
the accuracy and robustness of the camera-based pose estimation compared to the
robot's internal navigation stack, showing that our camera-based method
achieves pose errors below 3 cm and 1{\deg} and does not drift over time, as
the robot is localized allocentrically. With the robot's pose precisely
estimated, its observations can be fused into the allocentric scene model. We
show a real-world application, where observations from mobile robot and static
smart edge sensors are fused to collaboratively build a 3D semantic map of a
$\sim$240 m$^2$ indoor environment.
