Joint Learning of Feature Detector and Descriptor for Visual SLAM

Abstract

Visual Simultaneous Localization and Mapping is one of the main challenges for robotics and automated vehicles. In the state-of-the-art approaches, pixel-level correspondences are mostly used. In this paper, we address the problem of finding stable and repeatable pixel-level correspondences under challenging conditions. As a network basis, we use the D2-Net, which can select keypoints at positions that can be matched easily. Several techniques are implemented in this work to improve the keypoint detection and description performance. We first feed the network with rotated images to achieve a higher rotation invariance of point detections. Furthermore, we analyze the impact of a ranking of score values, adopting a cosine similarity and enforcing more dominant detections by defining a peakiness. The evaluation shows that combining a ranking with a peakiness can achieve the best result, especially to illumination changes. By using this combination, we achieve a mean matching accuracy increase of 12.5% on illumination scenes (9% overall) and a 9% higher repeatability rate at extremely low costs by only modifying the loss function.