A CLM-Based Method of Indoor Affordance Areas Classification for Service Robots

A representation and modeling method of indoor affordance areas based on CLM (codebookless model) is proposed to avoid using codebook. Firstly, multi-scale SURF (speeded-up robust feature) descriptors are extracted on grey-scale image. Then, the image is divided into some regular regions using the spatial pyramid method. By introducing Gaussian manifolds into vector space, each region is denoted as a single Gaussian model, and the mixed Gaussian model is combined to represent the whole image. Finally, the Gaussian model and the modified SVM (support vector machine) classifier are utilized to classify the indoor affordance areas. The experimental results on Scene 15 datasets show that the proposed method improves the classification accuracy by about 20% compared with the traditional codebook construction methods, is more robust to direction changes and uneven illumination, and effectively enhances the ability of service robots to cognize indoor affordance areas.