Solid-State LiDAR based-SLAM: A Concise Review and Application

Nam, Dinh Van; Kim, Gon–Woo

doi:10.1109/bigcomp51126.2021.00064

Cited by 47 publications

(23 citation statements)

References 7 publications

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“…Currently there are many popular SLAM algorithms, especially, the Lidar-based SLAM (Grisetti et al, 2007;Hess et al, 2016). The main feauture of the Lidar-based SLAM (Khan et al, 2021;Van Nam and Gon-Woo, 2021) is that it uses Lidar scanner to input the position data for mapping and localization. The Lidar-based SLAM consists of two main categories: 2D Lidar SLAM (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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

Pu¹,

Yang²,

Pu³

et al. 2023

Preprint

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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

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Section: Related Workmentioning

confidence: 99%

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

show abstract

“…After starting the industrial computer and establishing communication with the robot through the ROS system, the keyboard control node is started to control the car to move around the laboratory for one lap. The resulting grid map [10] is shown in Figure 6.…”

Section: Physical Environment Verificationmentioning

confidence: 99%

A navigation and mapping method for mobile robots based on 2D laser simultaneous localization and mapping algorithm

zhang,

liu,

kong

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Simultaneous localization and mapping (SLAM) and path planning are key technologies for robot navigation. The grid map generated by SLAM technology is a prerequisite for the path planning algorithm of mobile robots, playing an important role in autonomous robot navigation. 2D laser SLAM relies on distance information perceived by 2D laser radar to perform pose estimation and map construction, with advantages of high mapping accuracy and stable performance. In this article, we built an experimental platform using the ROS robot operating system, simulated and validated the Gmapping algorithm, and applied the validated algorithm to the Autolabor Pro1 robot platform for indoor mapping. The results show that the algorithm requires less computation and has higher accuracy in constructing small-scale maps, with good mapping effects.

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“…However, the satellite system is only able to provide a centimeter‐level precision; in addition, it becomes difficult to establish the location of a vehicle or robot when the signal is impeded by nearby structures. To enhance the reliability of localization, red‐green‐blue‐depth (RGB‐D) cameras (Liu et al., 2021), light detection and ranging (LiDAR; Lee, 2022), and inertial measurement unit (IMU) sensors (Neges et al., 2017) have been adopted for robotic indoor navigation mainly in indoor scenarios using simultaneous localization and mapping system (SLAM; Van & Gon‐Woo, 2021) and fiducial marker system (Kayhani et al., 2020). The SLAM has gained popularity since it enables the robots to explore the indoor (Filipenko & Afanasyev, 2018), underwater (Hidalgo & Bräunl, 2015), and outdoor environments (Bellés & Pla, 2015) and to carry out duties without human involvement.…”

Section: Introductionmentioning

confidence: 99%

Design and development of robotic collaborative system for automated construction of reciprocal frame structures

Chea,

Bai,

Zhou

2023

Computer aided Civil Eng

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Robotic technologies have shown their potential to improve efficiency, precision, and safety for construction tasks. In this paper, the concept of design for robotic construction (DfRC) is introduced, and robotic collaborative systems are developed for the construction of load‐carrying structures. An automated structural assembly was achieved and demonstrated through robotics with a preference for reciprocal frame (RF) structures. Key innovations include the use of magnetic temporary connections to minimize offsets of mobile robots and linear actuators as temporary supports during construction. Furthermore, specific procedures are formulated to navigate the robots and to target and install the components using a fiducial marker system and simultaneous localization and mapping packages, with consideration of structural deformation during construction due to self‐weight. The successful assembly of a 4.5‐m span RF structure demonstrates the potential of DfRC and the proposed robotic collaborative system in the automated construction of load‐carrying structures.

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Solid-State LiDAR based-SLAM: A Concise Review and Application

Cited by 47 publications

References 7 publications

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

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

A navigation and mapping method for mobile robots based on 2D laser simultaneous localization and mapping algorithm

Design and development of robotic collaborative system for automated construction of reciprocal frame structures

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