SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation
This dataset addresses the need for human behavior data in robotics and computer vision, but it is incremental as it provides a new resource without proposing novel methods.
The paper introduces a multimodal dataset collected from 108 human participants in a simulated shopping scenario to capture gestures and movements indicating navigational intent for autonomous robot navigation.
This article describes a dataset collected in a set of experiments that involves human participants and a robot. The set of experiments was conducted in the computing science robotics lab in Simon Fraser University, Burnaby, BC, Canada, and its aim is to gather data containing common gestures, movements, and other behaviours that may indicate humans' navigational intent relevant for autonomous robot navigation. The experiment simulates a shopping scenario where human participants come in to pick up items from his/her shopping list and interact with a Pepper robot that is programmed to help the human participant. We collected visual data and motion capture data from 108 human participants. The visual data contains live recordings of the experiments and the motion capture data contains the position and orientation of the human participants in world coordinates. This dataset could be valuable for researchers in the robotics, machine learning and computer vision community.