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

arXiv: https://arxiv.org/abs/2010.14802 · HTML: https://mlsanity.com/paper/2010.14802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Predicting Long-Term Human Behaviors in Discrete Representations via Physics-Guided Diffusion](https://mlsanity.com/paper/2405.19528/) (2024)
- [An Attention-based Model for Robust Forecasting with Missing Modality](https://mlsanity.com/paper/2606.13970/) (2026)

## Abstract (arXiv)

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.