-- In this letter, we propose a color-assisted robust framework for accurate LiDAR odometry and mapping (LOAM). Simultaneously receiving data from both the LiDAR and the camera, the framework utilizes the color information from the camera images to colorize the LiDAR point clouds and then performs iterative pose optimization. For each LiDAR scan, the edge and planar features are extracted and colored using the corresponding image and then matched to a global map. Specifically, we adopt a perceptually uniform color difference weighting strategy to exclude color correspondence outliers and a robust error metric based on the Welsch's function to mitigate the impact of positional correspondence outliers during the pose optimization process. As a result, the system achieves accurate localization and reconstructs dense, accurate, colored and three-dimensional (3D) maps of the environment. Thorough experiments with challenging scenarios, including complex forests and a campus, show that our method provides higher robustness and accuracy compared with current state-of-the-art methods. I. INTRODUCTION Light Detection and Ranging (LiDAR) has become one of the most critical perception modalities in robotic systems owing to its high accuracy, long range, and reliability. By enabling state estimation in six degrees of freedom (DoF) and construction of precise maps of the surrounding environment, LiDAR-based Simultaneous Localization and Mapping (SLAM) has found applications in autonomous driving [1], drone inspection [2], logistics [3], and other areas.