弱教師あり深層測位フレームワークDeepGPS
Deep Weakly Supervised Positioning
各観測の正解位置を使わず、車輪エンコーダの移動距離を制約とした弱教師あり学習でPoseNetを訓練し、2%未満の相対測位誤差を実現した。
著者: Ruoyu Wang, Xuchu Xu, Li Ding, Yang Huang, Chen Feng
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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/.
