SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation

TL;DR

This paper introduces SFU-Store-Nav, a multimodal dataset capturing human gestures, movements, and interactions with a robot in a shopping scenario, aimed at advancing autonomous robot navigation research.

Contribution

The paper presents a new multimodal dataset combining visual and motion data of humans interacting with robots in a shopping environment, useful for navigation and behavior analysis.

Findings

Collected data from 108 participants in a shopping scenario

Includes visual recordings and motion capture data

Dataset supports research in robotics, machine learning, and computer vision

Abstract

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.