P2-LOAM: LiDAR Odometry and Mapping with Pole-Plane Landmark

被引：0

作者：

Xu, Jianhong ^{[1
]}

Chen, Weinan ^{[1
]}

Mao, Shixin ^{[2
]}

Guan, Yisheng ^{[1
]}

Zhu, Haifei ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Electromech Engn, Biomimet & Intelligent Robot Lab BIRL, Guangzhou, Guangdong, Peoples R China

[2] Jiutian Innovat Guangdong Intelligent Technol Co, Foshan 528299, Peoples R China

来源：

2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024 | 2024年

基金：

中国国家自然科学基金;

关键词：

SLAM; pole landmark; SLAM;

D O I：

10.1109/ICIEA61579.2024.10665090

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For spatial perception, object-level SLAM (Simultaneous Localization and Mapping) has shown an advantage by leveraging semantic information to comprehend unknown environments. Poles are considered significant semantic landmark objects in urban roads and man-made constructions like stone pillars, street light lamps, and tree trunks. The rich pole landmarks enhance to the robustness and accuracy of SLAM. In this paper, we propose a LiDAR-based object-level SLAM named P-2-LOAM, which simultaneously estimates the pose and constructs a sparse pole landmark map. We propose a multi-RANSAC method for pole segmentation and estimate the parametric representation of pole objects in various scenes. Based on the segmented pole outcome, a coarse-to-fine data association for the pole object method is designed. Furthermore, a plane-assisted cost function for the pole landmark residual construction is developed. We demonstrate the accuracy and robustness of the proposed method in public datasets and real-world experiments.

引用

页数：7