Rein-SLAM: Narrow the Gaps Between the Matching Task and SLAM System

Image matching task and feature-based simultaneous localization and mapping (SLAM) systems both rely on feature points to complete pixel association, which provide an important support and foundation for the perception and integration of unmanned vehicles in the industrial park environments. In this article, we point out there exist associations and gaps between the matching task and SLAM systems. Based on the feature extraction network for matching task, we propose an effective training strategy in reference to the operating mode of SLAM systems, so that the feature extraction network can better adapt to the SLAM system for pose estimation and mapping. The proposed method only needs one sequence in the same scene for training, and the whole training process can be completed in only one epoch which takes no more than 20 min on an RTX 3080 GPU. We conduct experiments on HPatches dataset (matching task dataset), two public SLAM datasets: KITTI dataset for outdoor scenes and Euroc dataset for indoor scenes, and our collected dataset which contains several challenging scenes. Our experiments show that our method can not only effectively improve the performance of the extraction network on the original matching task but also greatly improve the performance of its integrated SLAM system. Furthermore, our method can be easily transplanted to other image matching networks, so that they can be better used in SLAM systems and narrow the gaps between the matching tasks and SLAM systems.

Automated summary

Image matching task and SLAM systems both rely on feature points for pixel association. This article proposes a training strategy based on the operating mode of SLAM systems, which enables the feature extraction network to better adapt to SLAM for pose estimation and mapping. The proposed method shows improved performance on both the matching task and integrated SLAM system, and can be easily applied to other image matching networks, narrowing the gaps between matching tasks and SLAM systems.