sSLAM – Speeded Up visual SLAM mixing artificial markers and temporary keypoints
Abstract
Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g. shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking.
This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers allowing a substantial reduction of the computing time and memory required, without noticeably degrading the tracking accuracy. In a first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just-in-time of tracking, that are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposal sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed without statistically significant differences in accuracy.
Cite us
sSLAM: Speeded Up visual SLAM mixing artificial markers and temporary keypoints. Romero-Ramirez, F. J.; Muñoz-Salinas, R., Marín-Jimenez M.J., Cazorla, M.; Medina-Carnicer R. Sensors 2023, 23, 2210.
The original published version can be obtained here.
Code and datasets
From an engineering point of view, sSLAM is an evolution of UcoSLAM. As such, it includes all UcoSLAM features and adds the ability of using different cameras. The source code can be downloaded at the following link:
The datasets used for the experimental section of this paper can be downloaded from the following links: