Deep Weakly Supervised Positioning for Indoor Mobile Robots

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 it is possible to do so via constraint-based weak-supervision, leading to the proposed framework: DeepGPS. Particularly, using wheel-encoder-estimated distances traveled by a robot along with random straight line segments as constraints between PoseNet outputs, DeepGPS can achieve a relative positioning error of less than 2% for indoor robot positioning. 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 on indoor mobile robots and perform a comprehensive analysis of its robustness.