A Framework for Fast Prototyping of Photo-realistic Environments with Multiple Pedestrians

Conference paper (2023)

Authors

S. Casao Universidad de Zaragoza
Andrés Otero Universidad de Zaragoza
A. Serra Gomez Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Ana C. Murillo Universidad de Zaragoza
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Eduardo Montijano Universidad de Zaragoza
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 S. Casao, Andrés Otero, A. Serra Gomez, Ana C. Murillo, J. Alonso-Mora, Eduardo Montijano
DOI
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https://doi.org/10.1109/ICRA48891.2023.10160586
More Info
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Robotic applications involving people often require advanced perception systems to better understand complex real-world scenarios. To address this challenge, photo-realistic and physics simulators are gaining popularity as a means of generating accurate data labeling and designing scenarios for evaluating generalization capabilities, e.g., lighting changes, camera movements or different weather conditions. We develop a photo-realistic framework built on Unreal Engine and AirSim to generate easily scenarios with pedestrians and mobile robots. The framework is capable to generate random and customized trajectories for each person and provides up to 50 ready-to-use people models along with an API for their metadata retrieval. We demonstrate the usefulness of the proposed framework with a use case of multi-target tracking, a popular problem in real pedestrian scenarios. The notable feature variability in the obtained perception data is presented and evaluated.