室内环境下融合G-ICP与三维高斯溅射的视觉SLAM算法OA

To address the problem that traditional visual simultaneous localization and mapping(SLAM)algorithms in indoor environments are affected by highly reflective objects and low-texture regions,resulting in decreased localization accuracy and degraded mapping quality,a visual SLAM algorithm integrating 3D Gaussian splatting(3DGS)and general-ized iterative closest point(G-ICP)is proposed,named GICP-STAM.Firstly,3DGS and G-ICP algorithms are integrated for pose initialization.Secondly,keyframe selection is performed based on G-ICP registration results to remove keyframes with low information density.Finally,a λSSIM loss strategy is proposed for Gaussian pruning and densification to filter out abnormal map points and create new map points.Experimental validation is conducted using three public datasets of indoor environments.The experimental results show that,compared with the baseline algorithm SplaTAM,the absolute trajectory error root mean square error(ATE RMSE)is improved by 38%,11%,and 3%on the Replica,TUM-RGBD,and ScanNet datasets,respectively;the average peak signal-to-noise ratio(PSNR)on the Replica and ScanNet datasets is improved by 6%and 23%,respectively.The localization accuracy and mapping quality in indoor environments are significantly better than those of the baseline algorithm.