Abstract
Monocular SLAM(Simultaneous Localization and Mapping) systems have such advantages as low cost and light weight compared to stereo or laser range finder based SLAM systems. However they also have relatively large errors when they measure the distances between a vehicle and some distinct objects, which can lead to scale ambiguity or scale drift. In this paper, we suggest a scale drift-aware monocular SLAM system using CNN(Convolutional Neural Network) which is one of the key technologies of Deep Learning. CNN nowadays has proved its performances especially in computer vision. We have trained the system with plenty of images of some predetermined objects using CNN. And then we can measure the absolute distances between a vehicle and already known objects and resolve the scale drift problems. The suggested system has been evaluated by several experiments.
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