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Article
Publication date: 2 January 2024

Xiangdi Yue, Yihuan Zhang, Jiawei Chen, Junxin Chen, Xuanyi Zhou and Miaolei He

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and…

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Abstract

Purpose

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

Design/methodology/approach

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

Findings

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

Originality/value

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 23 January 2025

Xiangdi Yue, Jiawei Chen, Yihuan Zhang, Siming Huang, Jiaji Pan and Miaolei He

Over the decades, simultaneous localization and mapping (SLAM) techniques have been extensively researched and applied in robotic mapping. In complex environments, SLAM systems…

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Abstract

Purpose

Over the decades, simultaneous localization and mapping (SLAM) techniques have been extensively researched and applied in robotic mapping. In complex environments, SLAM systems using a single sensor, such as a camera or light detection and ranging (LiDAR), often cannot meet the accuracy and map consistency requirements. This study aims to propose a tightly-coupled LiDAR-inertial SLAM system, which aims to achieve higher accuracy and map consistency for robotic mapping in complex environments.

Design/methodology/approach

This paper presents TC-Mapper, a tightly coupled LiDAR-inertial SLAM system based on LIO-SAM. The authors introduce the normal distribution-based loop closure detection method to the original one (i.e. the radius search-based method), which can enhance the accuracy and map consistency for robotic mapping. To further suppress map drift in complex environments, this paper incorporates a gravity factor into the original factor graph. In addition, TC-Mapper introduces incremental voxels (iVox) as the point cloud spatial data structure.

Findings

Extensive experiments in public and self-collected data sets demonstrate that TC-Mapper has high accuracy and map consistency.

Originality/value

TC-Mapper has two types of loop closure detections: the normal distribution-based method for correcting large drifts and the radius search-based method for fine-stitching, which can achieve higher accuracy and map consistency. The authors introduce iVox as the point cloud spatial data structure, which strives to attain a balance between precision and efficiency to the greatest extent feasible.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

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