深度弱监督定位
Deep Weakly Supervised Positioning
摘要：
PoseNet可以将照片映射到拍摄的位置，这在机器人技术中很有吸引力。然而，训练PoseNet需要全面的监督，其中地面实况位置是不平凡的。我们是否可以在不知道每个观测的地面真实位置的情况下训练PoseNet？我们通过基于约束的弱监督证明了这是可能的，从而产生了所提出的框架：DeepGPS。特别是，使用车轮编码器估计的机器人沿着随机直线段行进的距离作为PoseNet输出之间的约束，DeepGPS可以实现小于2%的相对定位误差。此外，训练DeepGPS可以作为自动校准完成，几乎没有人参与，这比通常需要仔细和专家级手动校准的竞争方法更具吸引力。我们在模拟和真实数据集上进行了各种实验，以证明DeepGPS的普遍适用性，有效性和准确性，并对其鲁棒性进行了全面分析。我们的代码可在https://ai4ce.github.io/DeepGPS/上获得。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
