Towards Utilizing Deep Uncertainty In Traditional SLAM

Abstract

Recent advances in Simultaneous Localization and Mapping (SLAM) systems make robots behave intelligently in different situations. However, performing well in dynamic environments requires extraction of robust features from the static context of consecutive frames. Deep Learning, nowadays, helps to extract meaningful and robust feature representations. Using various Deep Learning architectures and retrieving the uncertainty information concerning dynamic objects of the scene, and the motion flow of the environment helps traditional SLAM extract robust features in dynamic environments. To our knowledge, this paper is the first paper to fuse various deep uncertainties in traditional SLAM to obtain robust features in dynamic environments and improve the accuracy of motion estimation for autonomous vehicles.