Deep Metric Learning for Ground Images
This work addresses the need for efficient and accurate self-localization in robotics, though it is incremental as it builds on existing ground texture methods.
The paper tackles the initial localization problem for robots using ground texture images by proposing a deep metric learning approach to retrieve overlapping reference images, achieving significantly better recall and improving the localization performance of a state-of-the-art method.
Ground texture based localization methods are potential prospects for low-cost, high-accuracy self-localization solutions for robots. These methods estimate the pose of a given query image, i.e. the current observation of the ground from a downward-facing camera, in respect to a set of reference images whose poses are known in the application area. In this work, we deal with the initial localization task, in which we have no prior knowledge about the current robot positioning. In this situation, the localization method would have to consider all available reference images. However, in order to reduce computational effort and the risk of receiving a wrong result, we would like to consider only those reference images that are actually overlapping with the query image. For this purpose, we propose a deep metric learning approach that retrieves the most similar reference images to the query image. In contrast to existing approaches to image retrieval for ground images, our approach achieves significantly better recall performance and improves the localization performance of a state-of-the-art ground texture based localization method.