Robust Lifelong Indoor LiDAR Localization using the Area Graph
Fujing Xie, S\"oren Schwertfeger

TL;DR
This paper presents a robust indoor LiDAR localization method using a hierarchical, semantic Area Graph map that is compact, stable over time, and effective in cluttered environments, outperforming traditional map representations.
Contribution
The novel use of an Area Graph map for lifelong indoor localization, combining semantic information with a compact hierarchical structure for improved robustness.
Findings
Effective localization in cluttered environments.
Outperforms traditional map-based localization methods.
Robust against environmental changes like moved furniture.
Abstract
Lifelong indoor localization in a given map is the basis for navigation of autonomous mobile robots. In this letter, we address the problem of robust localization in cluttered indoor environments like office spaces and corridors using 3D LiDAR point clouds in a given Area Graph, which is a hierarchical, topometric semantic map representation that uses polygons to demark areas such as rooms, corridors or buildings. This representation is very compact, can represent different floors of buildings through its hierarchy and provides semantic information that helps with localization, like poses of doors and glass. In contrast to this, commonly used map representations, such as occupancy grid maps or point clouds, lack these features and require frequent updates in response to environmental changes (e.g. moved furniture), unlike our approach, which matches against lifelong architectural…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization · Underwater Vehicles and Communication Systems
