S3LAM: Structured Scene SLAM
This work addresses the need for more accurate and interpretable SLAM systems in robotics and computer vision, though it is incremental as it builds upon ORB-SLAM2.
The authors tackled the problem of improving SLAM accuracy and robustness by integrating semantic segmentation into the system, resulting in enhanced camera pose estimation compared to state-of-the-art methods on public datasets.
We propose a new SLAM system that uses the semantic segmentation of objects and structures in the scene. Semantic information is relevant as it contains high level information which may make SLAM more accurate and robust. Our contribution is twofold: i) A new SLAM system based on ORB-SLAM2 that creates a semantic map made of clusters of points corresponding to objects instances and structures in the scene. ii) A modification of the classical Bundle Adjustment formulation to constrain each cluster using geometrical priors, which improves both camera localization and reconstruction and enables a better understanding of the scene. We evaluate our approach on sequences from several public datasets and show that it improves camera pose estimation with respect to state of the art.