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Representations and Benchmarking of Modern Visual SLAM Systems
Yuchen Cao1,2,3, Lan Hu1,2,3, Laurent Kneip2
1Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
Sensors (Basel, Switzerland)
|May 6, 2020
Summary
Spatial Artificial Intelligence (Spatial AI) advances Simultaneous Localisation And Mapping (SLAM) by recovering object-level scene composition and semantics. New datasets and metrics are introduced for evaluating these advanced Spatial AI systems.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Simultaneous Localisation And Mapping (SLAM) is crucial for mobile applications requiring environmental interaction and navigation.
- Traditional SLAM focuses on geometric 3D reconstruction and localization.
- Emerging Spatial AI aims for object-level scene understanding, including semantic annotations.
Purpose of the Study:
- To explore the representations and optimization challenges in Spatial AI.
- To introduce novel synthetic datasets with comprehensive ground truth for Spatial AI validation.
- To propose new evaluation metrics for joint geometric-semantic representations.
Main Methods:
- Discussion of Spatial AI representation and optimization structures.
- Development of synthetic datasets with object-level ground truth (shapes, poses, composition).
- Proposal and application of novel evaluation metrics for geometric-semantic SLAM.
Main Results:
- Introduction of synthetic datasets enabling detailed Spatial AI evaluation.
- Development of metrics for assessing joint geometric and semantic mapping.
- Application of metrics to a new semantic SLAM framework.
Conclusions:
- The proposed datasets and metrics are vital for evaluating current and future Spatial AI systems.
- This work aims to significantly advance research progress in Spatial AI.
- Facilitates more robust and semantically rich 3D scene understanding in mobile applications.
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