Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments

Lasitha Piyathilaka; Sarath Kodagoda; Karthick Thiyagarajan; Massimo Piccardi; D. M. G. Preethichandra; Umer Izhar

doi:10.1109/ACCESS.2023.3347418

Back

Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments

Journal article

Open access

Peer reviewed

Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments

Lasitha Piyathilaka, Sarath Kodagoda, Karthick Thiyagarajan, Massimo Piccardi, D. M. G. Preethichandra and Umer Izhar

IEEE Access, Vol.12, pp.868-877

2024

DOI: https://doi.org/10.1109/ACCESS.2023.3347418

Files and links (1)

pdf

Learning_Spatial_Affordances_From_3D_Point_Clouds_for_Mapping_Unseen_Human_Actions_in_Indoor_Environments1.94 MBDownload View

Published VersionCC BY-NC-ND V4.0, Open Access

Abstract

human activity recognition

human centered robotics

human-robot interaction

smart sensing

spatial affordances

Many indoor robots operate in environments designed to support human activities. Understanding probable human actions in such surroundings is crucial for facilitating better human-robot interactions. This article presents an innovative approach to map unseen human actions in indoor environments by leveraging spatial affordances learned from geometric features extracted from point clouds captured by 3D cameras. Instead of directly observing real people to understand human context, the method utilizes virtual human models and their interactions with the environment to uncover hidden human affordances. This approach proves to be efficient for learning the affordance map, even when dealing with highly imbalanced datasets. To achieve this, we employ a supervised learning model optimized for the F1 score, using the Structured-SVM (S-SVM) architecture. We conducted experiments with actual 3D scenes, evaluating various affordance types both qualitatively and quantitatively. The results show that the proposed S-SVM-based method outperforms other models, demonstrating its effectiveness in efficiently mapping human context in indoor environments. The S-SVM-based method outperformed other models, demonstrating efficient human context mapping in indoor environments.

Details

Title: Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments
Authors: Lasitha Piyathilaka (Corresponding Author) - Central Queensland University
Sarath Kodagoda (Author) - University of Technology Sydney
Karthick Thiyagarajan (Author) - University of Technology Sydney
Massimo Piccardi (Author) - University of Technology Sydney
D. M. G. Preethichandra (Author) - Central Queensland University
Umer Izhar (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: IEEE Access, Vol.12, pp.868-877
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2024
DOI: 10.1109/ACCESS.2023.3347418
ISSN: 2169-3536
Copyright note: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 99991898102621
Output Type: Journal article

Metrics

1 File views/ downloads

30 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites