Full metadata
Title
From SLAM to Spatial AI: Using Implicit 3D Latent Space Landmark Reconstruction for Robot Localization and Mapping
Description
Simultaneous localization and mapping (SLAM) has traditionally relied on low-level geometric or optical features. However, these features-based SLAM methods often struggle with feature-less or repetitive scenes. Additionally, low-level features may not provide sufficient information for robot navigation and manipulation, leaving robots without a complete understanding of the 3D spatial world. Advanced information is necessary to address these limitations. Fortunately, recent developments in learning-based 3D reconstruction allow robots to not only detect semantic meanings, but also recognize the 3D structure of objects from a few images. By combining this 3D structural information, SLAM can be improved from a low-level approach to a structure-aware approach. This work propose a novel approach for multi-view 3D reconstruction using recurrent transformer. This approach allows robots to accumulate information from multiple views and encode them into a compact latent space. The resulting latent representations are then decoded to produce 3D structural landmarks, which can be used to improve robot localization and mapping.
Date Created
2023
Contributors
- Huang, Chi-Yao (Author)
- Yang, Yezhou (Thesis advisor)
- Turaga, Pavan (Committee member)
- Jayasuriya, Suren (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
43 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187693
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Computer Science
System Created
- 2023-06-07 12:08:02
System Modified
- 2023-06-07 12:08:07
- 1 year 5 months ago
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