Why Semantics Matters: A Deep Study on Semantic Particle-Filtering Localization in a LiDAR Semantic Pole Map

In most urban and suburban areas, pole-like structures, such as tree trunks or utility poles, are ubiquitous. These structural landmarks are very useful for the localization of autonomous vehicles given their geometrical locations in maps and measurements from sensors. In this work, we aim at creati...

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Bibliographic Details
Published inIEEE transactions on field robotics Vol. 1; pp. 47 - 69
Main Authors Huang, Yuming, Gu, Yi, Xu, Chengzhong, Kong, Hui
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
Cameras
Detection algorithms
Laser radar
Light detection and ranging (LiDAR) segmentation
Lighting
localization and mapping
Location awareness
Particle filters
Point cloud compression
Semantics
Sensors
Simultaneous localization and mapping
Three-dimensional displays
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Summary:In most urban and suburban areas, pole-like structures, such as tree trunks or utility poles, are ubiquitous. These structural landmarks are very useful for the localization of autonomous vehicles given their geometrical locations in maps and measurements from sensors. In this work, we aim at creating an accurate map for autonomous vehicles or robots with pole-like structures as the dominant localization landmarks, hence called pole map. In contrast to the previous pole-based mapping or localization methods, we exploit the semantics of pole-like structures. Specifically, semantic segmentation is achieved by a new mask-range transformer network in a mask-classification paradigm. With the semantics extracted for the pole-like structures in each frame, a multilayer semantic pole map is created by aggregating the detected pole-like structures from all frames. Given the semantic pole map, we propose a semantic particle-filtering localization scheme for vehicle localization. Theoretically, we have analyzed why the semantic information can benefit the particle-filter localization, and empirically, it is validated on the public SemanticKITTI dataset that the particle-filtering localization with semantics achieves much better performance than the counterpart without semantics when each particle's odometry prediction and/or the online observation is subject to uncertainties at significant levels.
ISSN:2997-1101
2997-1101
DOI:10.1109/TFR.2024.3421392