We studied HueCode, i.e., a metamarker with multiple fiducial markers overlaid in different colored layers. HueCode consists of relative pose markers (e.g., AR markers) and additional information markers (e.g., QR codes) through overlaying within the area of a single marker. Robots can simultaneously recognize the relative pose as well as additional information required for movement and recognition from a single HueCode. However, conventional coloring schemes and recognition methods deal with degradation of the recognition performance by illumination. Thus, we propose HueCode2 to solve this problem. The new coloring scheme allows the use of any color that is easy to distinguish. The new recognition method uses support vector machines trained under various illumination conditions to identify colors. The experimental results show that these methods improve recognition rates over conventional HueCode under various illumination conditions including indoor and outdoor environment.