An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this article, we...

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Bibliographic Details
Published in:IEEE transactions on robotics 2023-08, Vol.39 (4), p.1-21
Main Authors: Wu, Yanmin, Zhang, Yunzhou, Zhu, Delong, Deng, Zhiqiang, Sun, Wenkai, Chen, Xin, Zhang, Jian
Format: Article
Language:English
Subjects:
Algorithms
Augmented reality
Centroids
data association
Decision making
Location awareness
Matching
Object oriented modeling
Outliers (statistics)
Perception
Point cloud compression
robotics
Robots
semantic mapping
Semantics
Simultaneous localization and mapping
Solid modeling
visual SLAM
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Summary:Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this article, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multimap matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3273180