In order to improve the robustness of simultaneous localization and mapping (SLAM) in different indoor scenes and deal with the challenges in extreme environments such as less texture and poor light. The visual odometry based on ORB algorithm is improved by using the improved fast automatic color enhancement (ACE) image enhancement technique. At the same time, the original image data, the image data enhanced by contrast limited adaptive histogram equalization (CLAHE), single scale retinex ( SSR), and the improved fast ACE were applied to different real scenes, such as stairwells, subterranean parking lots, and two comparison experiments based on image quality and feature extraction matching are done. The experimental results show that the quality of the image enhanced by the improved fast ACE is better than the other algorithms. After enhancement, the number of feature points of visual odometry (VO) is increased by a multiple order, the matching number is increased by 7% ~ 25% in extreme environments, and the robustness is improved.