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Using olfactory and Visual Information to Assist Mobile Robots in Classifying Ground Stains

The obstacle avoidance system of indoor mobile robots is difficult to identify the ground stains that might reduce the robotic environmental safety in households and medical applications. This paper proposes a new method of fusing the information from the camera and electronic nose (e-nose). A two-layer network is built for feature extraction and decision. The first layer consists of three branches: vision branch, olfaction branch, and fusion branch. They will extract public and private features from different modal information. The second layer determines the weights for the output of the first layer. Two experiments are implemented to verify the proposed model. The results show that the proposed method provides an effective, practical, and promising solution for improving the vision-based ground stain identification.