A model of the category-specific higher layer of a range-data-based hierarchical representation of space is presented. A method for discriminative exemplar learning based on pair-wise part similarities is introduced and applied for part dictionary selection. The method is general and can easily be applied to other modalities. Furthermore, an approach for range-data-based room categorization using the category-specific parts is proposed..

The next generation service robots are expected to co-exist with humans in their homes. Such a mobile robot requires an efficient representation of space, which should be compact and expressive, for effective operation in real-world environments. We present the category-independent lower layers of a novel compositional hierarchical representation of space that is based on 2D ground-plan-like laser-range-data. The effectiveness of the model is demonstrated in the context of room categorization problem.

A service robot that operates in a previously-unseen home environment should be able to recognize the functionality of the rooms it visits, such as a living room, a bathroom, etc. We present a novel part-based model and an approach for room categorization using data obtained from a visual sensor. The proposed approach uses a convolutional neural network (CNN) and is robust to image degradation, such as occlusions, modifications of image scaling, and aspect changes.

We developed algorithms and representations for autonomous mapping and visual localization of mobile robots from omni-directional images. We also developed a purely emergent hierarchical mapping algorithm based on “recover and select” that creates maps of wide and unstructured environments from local appearance and odometry.