SRIN: A New Dataset for Social Robot Indoor Navigation

Generating a cohesive and relevant dataset for a particular robotics application is regarded as a crucial step towards achieving better performance. In this communication, we propose a new dataset referred to as SRIN, which stands for Social Robot Indoor Navigation. This dataset consists of 2D colored images for room classification (termed SRIN-Room) and doorway detection (termed SRIN-Doorway). SRIN-Rooms has 37,288 raw and processed colored images for five main classes: bedrooms, bathrooms, dining rooms, kitchens, and living rooms. The SRIN-Doorway contains 21,947 raw and processed colored images for three main classes: no-door, open-door and closed door. The main feature of SRIN dataset is that its images have been purposefully captured for short robots (around 0.5-meter tall) such as NAO humanoid robots. All images of the first version of SRIN were collected from several houses in Vancouver, BC, Canada. The methodology of collecting SRIN was designed in a way that facilitates generating more samples in the future regardless of where the samples have come from. For a validation purposes, we trained a CNN-based model on SRIN-Room dataset, and then tested it on Nao humanoid robot. The Nao prediction results in this paper are presented and compared with the prediction results using the same model with Places-dataset. The results suggest an improved performance for this class of humanoid robots.

Keywords: Social robots; Nao; SRIN-dataset; CNN; Room classification