An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Abstract
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 paper, 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 multi-map 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.
Cite
@article{arxiv.2305.07299,
title = {An Object SLAM Framework for Association, Mapping, and High-Level Tasks},
author = {Yanmin Wu and Yunzhou Zhang and Delong Zhu and Zhiqiang Deng and Wenkai Sun and Xin Chen and Jian Zhang},
journal= {arXiv preprint arXiv:2305.07299},
year = {2023}
}
Comments
Accepted by IEEE Transactions on Robotics(T-RO)