  This paper presents a novel WiFi-Visual data fusion method for indoor robot
(TIAGO++) localization. This method can use 10 WiFi samples and 4
low-resolution images ($58 \times 58$ in pixels) to localize a indoor robot
with an average error distance about 1.32 meters. The experiment test is 3
months after the data collection in a general teaching building, whose WiFi and
visual environments are partially changed. This indirectly shows the robustness
of the proposed method.
  Instead of neural network design, this paper focuses on the soft data fusion
to prevent unbounded errors in visual localization. A double-layer soft data
fusion is proposed. The proposed soft data fusion includes the first-layer
WiFi-Visual feature fusion and the second-layer decision vector fusion.
Firstly, motivated by the excellent capability of neural network in image
processing and recognition, the temporal-spatial features are extracted from
WiFi data, these features are represented in image form. Secondly, the WiFi
temporal-spatial features in image form and the visual features taken by the
robot camera are combined together, and are jointly exploited by a
classification neural network to produce a likelihood vector for WiFi-Visual
localization. This is called first-layer WiFi-Visual fusion. Similarly, these
two types of features can exploited separately by neural networks to produce
another two independent likelihood vectors. Thirdly, the three likelihood
vectors are fused by Hadamard product and median filtering to produce the final
likelihood vector for localization. This called the second-layer decision
vector fusion. The proposed soft data fusion does not apply any threshold or
prioritize any data source over the other in the fusion process. It never
excludes the positions of low probabilities, which can avoid the information
loss due to a hard decision. The demo video is provided. The code will be open.
