RISED: Accurate and Efficient RGB-Colorized Mapping Using Image Selection and Point Cloud Densification

Abstract

Recent advances in robotics have underscored the critical role of colorized point clouds in enhancing environmental perception accuracy. However, conventional multisensor fusion Simultaneous Localization and Mapping (SLAM) systems typically employ all available images indiscriminately for point cloud colorization, resulting in suboptimal outcomes with blurred textures. Notably, achieving precise texture-togeometry alignment remains a challenge despite the availability of accurate pose estimation. This study introduces RISED, an advanced colorized mapping system that tackles this challenge from two perspectives: projection accuracy and distribution uniformity. For projection accuracy, we analyze the influence of camera poses on colorization and carefully select the optimal viewpoint to minimize errors. Regarding distribution uniformity, point cloud densification is applied to eliminate LiDAR scanning traces. Furthermore, a novel evaluation method is introduced to provide comprehensive assessment of colorized point clouds, filling a gap in this field. Experimental results show that our method outperforms traditional approaches in RGB-colorized mapping. Specifically, our method achieves notable improvements in projection accuracy (55.2 %), geometric accuracy (63.1 %), and surface coverage (30.8 %).