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This paper deals with the problem of modeling spatial uncertainty of point features in feature-based RGB-D SLAM. Although the feature-based approach to SLAM is very popular, in the case of systems using RGB-D data the problem of explicit uncertainty modeling is largely neglected in the implementations. Therefore, we investigate the influence of the uncertainty models of point features on the accuracy of the estimated trajectory and map. We focus on the recent SLAM formulation employing factor graph optimization. Unlike some visual SLAM systems employing factor graph optimization that minimize the reprojection errors of features, we explicitly use depth measurements and minimize the errors in the 3-D space. The paper analyzes the impact of the information matrices used in factor graph optimization on the achieved accuracy. We introduce three different models of point feature spatial uncertainty. Then, applying the most simple model, we demonstrate in simulations how important is the influence of the spatial uncertainty model on the graph optimization results in an idealized SLAM system with perfect feature matching. A novel software tool allows us to visualize the statistical behavior of the features over time in a real SLAM system. This enables the analysis of the distribution of feature measurements employing synthetic RGB-D data processed in an actual SLAM pipeline. Finally, we show on publicly available real RGB-D datasets how an uncertainty model, which reflects the properties of the RGB-D sensor and the image processing pipeline, improves the accuracy of sensor trajectory estimation.

Modeling spatial uncertainty of point features in feature-based RGB-D SLAM