In this work, we study the loop detection problem for mobile robots in the process of simultaneous localization and mapping. In a complex environment, the accuracy and recall rate of loop detection will decrease significantly because of the lack of prominent environmental state information. The proposed method combines light fidelity (Li-Fi), used for sensing the position of mobile robots, with laser ranging technology to recognize the location that the robot has been aware of during the period of locating and mapping. A shortest link processing mechanism for Li-Fi calibration is proposed to select data, thus smoothing the system. Using an extended Kalman filter, we construct virtual consistent state components of Li-Fi information. Scans are enriched by providing more environmental information in this method. A distance-region model is designed to reduce the amount of calculation appropriately in the process of scan matching. The transformation between frames is reconstructed using a weight distribution from the data of laser and photoelectric sensors to reduce the error of data association. To reduce the impact of Li-Fi link occlusion, we set up a pause mechanism during the period of fusing information in the process of scan-to-map matching. Results of experiments in corridors and offices show that the proposed method can detect key frames, effectively suppress pose drift, improve the accuracy, and guarantee a satisfactory recall rate of loop closure detection.