Lightweight Object-level Topological Semantic Mapping and Long-term
Global Localization based on Graph Matching
- URL: http://arxiv.org/abs/2201.05977v1
- Date: Sun, 16 Jan 2022 05:47:07 GMT
- Title: Lightweight Object-level Topological Semantic Mapping and Long-term
Global Localization based on Graph Matching
- Authors: Fan Wang, Chaofan Zhang, Fulin Tang, Hongkui Jiang, Yihong Wu, and
Yong Liu
- Abstract summary: We present a novel lightweight object-level mapping and localization method with high accuracy and robustness.
We use object-level features with both semantic and geometric information to model landmarks in the environment.
Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor.
- Score: 19.706907816202946
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mapping and localization are two essential tasks for mobile robots in
real-world applications. However, largescale and dynamic scenes challenge the
accuracy and robustness of most current mature solutions. This situation
becomes even worse when computational resources are limited. In this paper, we
present a novel lightweight object-level mapping and localization method with
high accuracy and robustness. Different from previous methods, our method does
not need a prior constructed precise geometric map, which greatly releases the
storage burden, especially for large-scale navigation. We use object-level
features with both semantic and geometric information to model landmarks in the
environment. Particularly, a learning topological primitive is first proposed
to efficiently obtain and organize the object-level landmarks. On the basis of
this, we use a robot-centric mapping framework to represent the environment as
a semantic topology graph and relax the burden of maintaining global
consistency at the same time. Besides, a hierarchical memory management
mechanism is introduced to improve the efficiency of online mapping with
limited computational resources. Based on the proposed map, the robust
localization is achieved by constructing a novel local semantic scene graph
descriptor, and performing multi-constraint graph matching to compare scene
similarity. Finally, we test our method on a low-cost embedded platform to
demonstrate its advantages. Experimental results on a large scale and
multi-session real-world environment show that the proposed method outperforms
the state of arts in terms of lightweight and robustness.
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