Abstract
A major focus of current research on place recognition is visual localization for autonomous driving, which must be robust against significant appearance change. This work makes three contributions towards solving visual localization under appearance change: i) We present G2D, a software that enables capturing videos from Grand Theft Auto V, a popular role playing game set in an expansive virtual city. The target users of our software are robotic vision researchers who wish to collect hyper-realistic computer-generated imagery of a city from the street level, under controlled 6 DoF camera poses and different environmental conditions; ii) Using G2D, we construct a synthetic dataset simulating a realistic setting, i.e., multiple vehicles traversing through a road network in an urban area under different environmental conditions; iii) Based on image retrieval using local features and an encoding technique, a novel Monte Carlo localization algorithm is proposed. The experimental results show that our proposed method achieves better results than state-of-the-art approaches for the task on visual localization under significant appearance change. The dataset will be available online upon acceptance. G2D is made available at: https://github.com/dadung/G2D
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URL
http://arxiv.org/abs/1811.08063