Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching

TL;DR

This paper introduces a lightweight, object-level topological mapping and localization method that leverages semantic and geometric features, enabling robust large-scale navigation without prior geometric maps, suitable for resource-limited robots.

Contribution

It proposes a novel environment representation as a semantic topology graph and a hierarchical memory mechanism for efficient, robust localization on low-cost platforms.

Findings

Outperforms state-of-the-art methods in robustness and lightweight design

Effective in large-scale, multi-session real-world environments

Operates efficiently on embedded platforms

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.