Visual localization based on sequence matching using ConvNet features

Abstract

Recently, Convolutional Network (ConvNet) features permit to achieve state-of-the-art performance in robotic fields such as visual navigation and SLAM. In this paper, a visual localization technique was proposed based on ConvNet networks by combining the powerful ConvNet features and image sequence matching. The pre-trained networks provided by MatConvNet are used to extract the features and then a sequence search technique is applied for visual recognition. Compared with the traditional approaches based on handcraft features and single image matching, the proposed method shows good performances even in presence of appearance and illumination changes. We present extensive experiments on five real world datasets to evaluate each of the specific challenges in visual recognition. We also conduct a comprehensive performance comparison of different ConvNet layers (each defining a level of features) considering both appearance and illumination changes.