Application of CRF and SVM based semi-supervised learning for semantic labeling of environments

Understanding the environment in both geometric and semantic levels enables a robot to perform high-level tasks in complex environments. Therefore in recent years research towards identifying and semantically labeling the environments based on onboard sensors for mobile robots has been gaining popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms like Support Vector Machines (SVM) and AdaBoost have been extensively used for this purpose showing satisfactory performance. With the introduction of graphical models, approaches like Conditional Random Fields (CRF) which take the advantage of connectivity of samples provide more flexibility to capture complex dependencies. In this paper, we focus on a real-world task which challenges the generalization ability of the model, evaluate some graph based features, propose a semi-supervised learning algorithm by iteratively utilizing the results from SVM and CRF, and suggest a solution for CRF parameter estimation with partially labeled training data. Experiments have been conducted on six real-world indoor environments demonstrating the competence of the algorithm.