Deep Weakly Supervised Positioning

• URL: http://arxiv.org/abs/2104.04866v1
• Date: Sat, 10 Apr 2021 21:19:08 GMT
• Title: Deep Weakly Supervised Positioning
• Authors: Ruoyu Wang, Xuchu Xu, Li Ding, Yang Huang, Chen Feng
• Abstract summary: 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.
• Score: 19.98491876054782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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