In this paper, we propose a WiFi-Visual robot localization method for limiting the unbounded error of imageonly localization due to visual environment similarity. The localization problem is modeled as a classification problem based on the WiFi-Visual data collected at labelled positions. The heterogeneous WiFi-Visual data is harmonized by representing the WiFi features in image form to adapt to the strong image processing capacity of the neural network. The WiFi features in image form are fused with the visual features provided by the robot camera. The fused WiFi-Visual features are jointly exploited by a neural network to classify WiFi-Visual features of an unknown position to the most likely class. The labelled position corresponding to the most likely class is taken as the estimated position of the robot. Experiments are carried out on the physical robot platform TIAGO++, which can provide the real-time ground truth reference position. Experiment results show that the proposed WiFi-Visual data fusion method can effectively limit the exceptional unbounded localization errors of image-only localization. The RMSE of the proposed method is less than 2 meters. This value is smaller than that of WiFi localization. The proposed method has more stable performance than WiFi-only localization and image-only localization. Its performance can be further improved by Kalman filtering.