Statistical localization exploiting convolutional neural network for an autonomous vehicle

In this paper, we propose a self-localization method that exploits object recognition results by using convolutional neural networks (CNNs) for autonomous vehicles. Monte-Carlo localization (MCL) is one of the most popular localization methods that use odometry and distance sensor data for determining vehicle position. Some errors are often observed in the localization tasks and MCL often suffers from global positional errors. A global positional error means that particles representing a vehicle's position are distributed in the form of a multimodal distribution, i.e., the distribution has several peaks. To overcome this problem, we propose a method in which an autonomous vehicle employs object recognition results, obtained using CNNs, as the measurement data with a Bag-of-Features representation in an integrative manner. The semantic information found in the recognition results obtained using the CNN reduces the global errors in localization. The experimental results show that the proposed method can converge the distribution of the vehicle positions and particle orientations and reduce the global positional errors.