FC-vSLAM: Integrating Feature Credibility in Visual SLAM

Feature-based visual SLAM (vSLAM) systems compute camera poses and scene maps by detecting and matching 2D features, mostly being points and line segments, from image sequences. These systems often suffer from unreliable detections. In this paper, we define feature credibility (FC) for both points and line segments, formulate it into vSLAMs and develop an FC-vSLAM system based on the widely used ORB-SLAM framework. Compared with existing credibility definitions, the proposed one, considering both temporal observation stability and perspective triangulation reliability, is more comprehensive. We formulate the credibility in our SLAM system to suppress the influences from unreliable features on the pose and map optimization. We also present a way to improve the line end observations by their multi-view correspondences, to improve the integrity of the 3D maps. Experiments on both the TUM and 7-Scenes datasets demonstrate that our feature credibility and the multi-view line optimization are effective; the developed FC-vSLAM system outperforms existing popular feature-based systems in both localization and mapping.