A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies

Khan, Misha Urooj; Zaidi, Syed Azhar Ali; Ishtiaq, Arslan; Bukhari, Syeda Ume Rubab; Samer, Sana; Farman, Ayesha

doi:10.1109/majicc53071.2021.9526266

Cited by 81 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Prior studies have suggested robotic explorer solutions. In addition, many academic and industrial works have provided successful theories, methods, algorithms, and hardware to solve the complexities of exploring deep wells and caves [1][2][3]. Unfortunately, all these solutions have a relatively high cost that might not be accessible for local populations.…”

Section: Introductionmentioning

confidence: 99%

“…Authors of [14,15] demonstrated the applicability and efficiency of robotic exploratory capabilities in complex environments by using a LiDAR-based system that has been used on simulated and real environments. Authors of [3] provided a competitive survey on LiDAR-SLAM based sensor technologies. Their results showed remarkable challenges in using the LiDAR-SLAM system, which were: 1) LiDAR could not provide accurate localization due to a lack of precision; 2) LiDAR suffered from time complexity in performing and computing the gathered data; 3) the accuracy of location was more accurate in 3D than 2D and 4) LiDAR could not easily identify one specific object out of a group of objects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

Alhmiedat¹,

Marei²,

Albelwi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Agricultural projects in different parts of the world depend on underground water wells. Recently, there have been many unfortunate incidents in which children have died in abandoned underground wells. Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them, especially since most of these wells become buried over time. Many solutions have been developed recently, most with the aim of exploring these well areas. However, these systems suffer from several limitations, including high complexity, large size, or inefficiency. This paper focuses on the development of a smart exploration unit that is able to investigate underground well areas, build a 3D map, search for persons and animals, and determine the levels of oxygen and other gases. The exploration unit has been implemented and validated through several experiments using various experiment testbeds. The results proved the efficiency of the developed exploration unit, in terms of 3D modeling, searching, communication, and measuring the level of oxygen. The average accuracy of the 3D modeling function is approximately 95.5%. A benchmark has been presented for comparing our results with related works, and the comparison has proven the contributions and novelty of the proposed system's results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

Alhmiedat¹,

Marei²,

Albelwi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Nie et al, 2021;Wang et al, 2022). Additionally, the localization accuracy from SLAM (Simultaneous Localization And Mapping) (Bai et al, 2021(Bai et al, , 2022Grisetti et al, 2007;Hess et al, 2016;Khan et al, 2021;Nubert et al, 2022;Van Nam and Gon-Woo, 2021) is not high enough for precise parking in MMPA. Matching the 2D template marker's images from the teaching and automation stages (Meng et al, 2021) could provide a relative 3D pose for parking error compensation but can only be used to move the robot base on a 2D plane rather than a 3D surface.…”

Section: Introductionmentioning

confidence: 99%

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

Pu¹,

Yang²,

Pu³

et al. 2023

Preprint

View full text Add to dashboard Cite

To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact

show abstract

“…SLAM based on all kinds of laser radar is not within the scope of discussion in this paper. Interested readers can refer to [6][7][8] and other sources in the literature.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

View full text Add to dashboard Cite

Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

show abstract

A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies

Cited by 81 publications

References 28 publications

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

An Overview on Visual SLAM: From Tradition to Semantic

Contact Info

Product

Resources

About