SVPLP-SLAM:一种基于点-线-面特征融合并带有假定平面约束的RGB-D视觉SLAMOA

Traditional RGB-D Visual Simultaneous Localization and Mapping(vSLAM)systems rely pre-dominantly on a single kind of feature or a simplistic combination of features.However,such approaches of-ten fail to achieve optimal accuracy and mapping completeness,particularly in scenarios characterized by low texture and significant noise.In contrast,the fusion of multiple features has shown superior performance in mitigating localization inaccuracies while simultaneously enhancing robustness.Therefore,this paper pro-poses a novel RGB-D Visual SLAM system Using Point-Line-Plane Feature Fusion with Supposed Plane Constraints(SVPLP-SLAM)that synergistically integrates point features,line features,and plane features for localization and mapping.The system uses an RGB-D camera to extract point features and line features from the RGB images,and plane features from the depth images.By employing correlation matching and length suppression strategies,invalid long-distance short line features can be eliminated.Using supposed planes as edge constraints,the system can obtain plane features with orthogonal structures.The introduction of line features and plane features can effectively decrease the number of features required by the tracking mod-ule,thereby reducing the computational complexity of Bundle Adjustment(BA)optimization.Finally,using the root mean square error(RMSE)of absolute trajectory errors(ATEs)as a basis,extensive experiments on the TUM RGB-D and ICL-NUIM datasets verify that the proposed method outperforms current main-stream methods in terms of localization accuracy.