PLUE-SLAM: An RGB-D SLAM System Integrating Uncertainty Estimation of Point and Line Features

Many RGB-D simultaneous localization and mapping (SLAM) systems solely rely on point features, suffering from performance degradation in weak-texture scenes where line features can be complementary. However, existing RGB-D SLAM methods fusing point and line features commonly fail to explicitly estimate the feature uncertainties, hindering an accurate evaluation of both reliabilities and contributions of different features. In this paper, an RGB-D SLAM approach termed PLUE-SLAM integrating the closed-form uncertainty estimation of point and line features is proposed. Specifically, PLUE-SLAM derives the uncertainties of point and line features by jointly considering image observations, depth measurements and triangulation. In the front-end tracking, feature uncertainties provide an adaptive weighting mechanism for feature fusion, and are further propagated to the uncertainties of the camera poses. The uncertainties of both features and poses are used to weight the cost function in local bundle adjustment (BA) optimization, and are subsequently updated. Finally, a loop closure detection method using the feature uncertainties is designed to improve the robustness of PLUE-SLAM. Extensive experiments on public datasets and in real-world environments demonstrate that PLUE-SLAM outperforms state-of-the-art visual SLAM systems. PLUE-SLAM is open-sourced to benefit the community1.