Place categorization using sparse and redundant representations

Place categorization addresses the problem of determining the semantic label of the current position of a robot, given a snapshot of the environment as well as previously labeled information about different places that the robot has already seen. State-of-the-art approaches use machine learning techniques that require extensive and often time consuming training. This work proposes a novel formulation by posing place categorization as an efficient ℓ₁-minimization problem, leading to both a faster training phase and to performance comparable to state-of-the-art methods. The formulation allows online robot operation particularly in the case when the training phase has to be learned on-the-fly and in an active manner. To validate the performance of the proposed method, extensive experimental results carried out on real data under different lighting conditions as well as structural changes in the environment are provided.