Iris Publishers - Global Journal of Engineering Sciences (GJES)

SRIN: A New Dataset for Social Robot Indoor Navigation


Authored by Ahmad B Rad



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.

Providing a seamless and reliable solution to indoor navigation is a central research problem in robotics as resolving this challenge is a precursor for success of many activities of a social robot. Indeed, achieving the ultimate objective of having a social robot in every home depends on a reliable solution to this problem. Social robots will be part of the family as pets are. They interact and assist in chores and will keep company for minors and seniors. Within this context, they must be able to flawlessly roam around the home and be able to identify different locations and their functionalities in a house. The authors of this communication presented a CNNbased model (Convolutional Neural Network) that demonstrated promising results with respect to room classification in an indoor setting [1]. As training a CNN-model requires a significant number of samples, there are many models trained on popular computer visions datasets, such as ImageNet [2] and Places [3]. However, adopting pre-trained CNN models that learned features from computer vision datasets to be tested in real-time experiments on social robots, e.g. Nao humanoid robot [4] was not overwhelmingly successful [1]. We suggest that a dedicated dataset as opposed to general datasets such as ImageNet or Places could drastically improve the performance in real-time experiments.

 

To read more about this article: https://irispublishers.com/gjes/fulltext/srin-a-new-dataset-for-social-robot.ID.000596.php

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