High accuracy map matching method using monocular cameras and low-end GPS-IMU systems

단안 카메라와 저정밀 GPS-IMU 신호를 융합한 맵매칭 방법

  • Received : 2018.03.16
  • Accepted : 2018.04.06
  • Published : 2018.04.30


This paper presents a new method to estimate the pose of a moving object accurately using a monocular camera and a low-end GPS+IMU sensor system. For this goal, we adopted a deep neural network for the semantic segmentation of input images and compared the results with a semantic map of a neighborhood. In this map matching, we use weight tables to deal with label inconsistency effectively. Signals from a low-end GPS+IMU sensor system are used to limit search spaces and minimize the proposed function. For the evaluation, we added noise to the signals from a high-end GPS-IMU system. The results show that the pose can be recovered from the noisy signals. We also show that the proposed method is effective in handling non-open-sky situations.


Augmented Reality;Deep Learning;Map Matching;Road Detection;Semantic Segmentation


Supported by : 한화시스템


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