Recent advances in robotics have underscored the critical role of colorized point clouds in enhancing environmental perception accuracy. However, conventional multi-sensor fusion Simultaneous Localization and Mapping (SLAM) systems typically employ all available camera images indiscriminately for point cloud colorization, resulting in suboptimal outcomes characterized by blurred textures. Notably, achieving precise texture-to-geometry alignment remains a persistent challenge despite the availability of accurate camera 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 both accuracy and efficiency 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%).

Our system consists of two threads: LiDAR-based SLAM and colorized mapping. The LiDAR-based SLAM utilizes data from two LiDAR sensors and an IMU to estimate poses at the frequency of the IMU and generate dense point clouds. The colorized mapping thread incorporates two innovative processes: image selection and point cloud densification. Additionally, we introduce a novel approach to evaluate the quality of colorized point clouds. This method integrates three important factors: projection accuracy, geometric accuracy, and surface coverage.