Exploiting SLAM to improve feature matching

One of the main problems associated with vision-based SLAM is the difficulty to find high-quality and indistinguishable landmarks which are detectable across significant viewpoint changes. Most of the current visual SLAM systems utilize the feature extraction methods that have been proposed for the offline computer vision tasks, which are either too slow for a real-time SLAM or not robust enough against viewpoint changes. In this paper, we demonstrate that by incorporating the position information of the robot into the feature description, we can help the feature matching process significantly.