  PoseNet can map a photo to the position where it is taken, which is appealing
in robotics. However, training PoseNet requires full supervision, where ground
truth positions are non-trivial to obtain. Can we train PoseNet without knowing
the ground truth positions for each observation? We show that this is possible
via constraint-based weak-supervision, leading to the proposed framework:
DeepGPS. Particularly, using wheel-encoder-estimated distances traveled by a
robot along random straight line segments as constraints between PoseNet
outputs, DeepGPS can achieve a relative positioning error of less than 2%.
Moreover, training DeepGPS can be done as auto-calibration with almost no human
attendance, which is more attractive than its competing methods that typically
require careful and expert-level manual calibration. We conduct various
experiments on simulated and real datasets to demonstrate the general
applicability, effectiveness, and accuracy of DeepGPS, and perform a
comprehensive analysis of its robustness. Our code is available at
https://ai4ce.github.io/DeepGPS/.
