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Conference proceeding
Active Metric-Semantic Mapping by Multiple Aerial Robots
2023 IEEE International Conference on Robotics and Automation (ICRA), v 2023-, pp 3282-3288
29 May 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Traditional approaches for active mapping focus on building geometric maps. For most real-world applications, however, actionable information is related to semantically meaningful objects in the environment. We propose an approach to the active metric-semantic mapping problem that enables multiple heterogeneous robots to collaboratively build a map of the environment. The robots actively explore to minimize the uncertainties in both semantic (object classification) and geometric (object modeling) information. We represent the environment using informative but sparse object models, each consisting of a basic shape and a semantic class label, and characterize uncertainties empirically using a large amount of real-world data. Given a prior map, we use this model to select actions for each robot to minimize uncertainties. The performance of our algorithm is demonstrated through multi-robot experiments in diverse real-world environments. The proposed framework is applicable to a wide range of real-world problems, such as precision agriculture, infrastructure inspection, and asset mapping in factories.
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Details
- Title
- Active Metric-Semantic Mapping by Multiple Aerial Robots
- Creators
- Xu Liu - University of PennsylvaniaAnkit Prabhu - University of PennsylvaniaFernando Cladera - University of PennsylvaniaIan D. Miller - University of PennsylvaniaLifeng Zhou - University of PennsylvaniaCamillo J. Taylor - University of PennsylvaniaVijay Kumar - University of Pennsylvania
- Publication Details
- 2023 IEEE International Conference on Robotics and Automation (ICRA), v 2023-, pp 3282-3288
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- EEC-1941529 / National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001036713002095
- Scopus ID
- 2-s2.0-85168675956
- Other Identifier
- 991021945759404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Robotics