Application of semi-supervised learning with Voronoi Graph for place classification

Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively used for this purpose with SVM-based solutions showing good generalization properties. The CRF-based approaches take the advantage of connectivity information of samples thereby provide a mechanism to capture complex dependencies. Blending the complementary strengths of Support Vector Machine (SVM) and Conditional Random Field (CRF), there have been algorithms to exploit the advantages of both to enhance the overall accuracy of place classification in indoor environments. However, experiments show that none of the above approaches deal well with diversified testing data. In this paper, we focus mainly on the generalization ability of the model and propose a semi-supervised learning strategy, which essentially improves the performance of the system. Experiments have been carried out on six real-world maps from different universities around the world and the results from rigorous testing demonstrate the feasibility of the approach.