ORB-TEDM: An RGB-D SLAM Approach Fusing ORB Triangulation Estimates and Depth Measurements

Abstract

3-D position estimates of feature points in traditional RGB-D simultaneous localization and mapping (SLAM) systems are directly obtained by depth measurements. However, the available information provided by the triangulation of feature points has not been involved. In this article, a novel RGB-D SLAM approach is proposed based on the ORB-SLAM2 system, by fusing the triangulation estimates and depth measurements of ORB features, termed ORB-TEDM. Specifically, uncertainties on both the pose estimate of the camera and triangulation of ORB features are rigorously computed in a closed form for the ORB-SLAM2 system using covariance propagation and implicit differentiation. On the other hand, the uncertainty is evaluated for the 3-D position estimate of each map point obtained by its depth measurement from the RGB-D camera. Then, the triangulation results and position estimates from depth measurements are further fused with the covariance intersection (CI) filter to generate a more precise and consistent map. In addition, a more flexible selection policy of map points and keyframes is designed based on the uncertainty evaluation results. As a consequence, a more accurate and robust RGB-D SLAM system can be achieved. It is worthwhile to point out that the obtained closed-form solution to uncertainties on estimates of the camera pose and feature points can facilitate further development of the ORB-SLAM framework, such as active SLAM and multisensor SLAM. The experimental results on public datasets and in real-world environments are presented to show the effectiveness of the proposed approach.