A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Yu, Shan; Fu, Chunyun; Gostar, Amirali Khodadadian; Hu, Minghui

doi:10.3390/s20236988

Cited by 38 publications

(23 citation statements)

References 107 publications

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“…Their wide usage for SLAM came from the brilliant idea of the Rao-Blackwellization method, which was introduced into regular map-building algorithms [37]. Although the particle filter algorithm can be used as an effective method to solve the SLAM problem [9,13,18,21,38], the main problem is that a large number of samples are required to approximate the posterior probability density of the system. The more complex the environment that the robot meets, the more samples that are needed to describe the posterior probability, and the higher the complexity of the algorithm.…”

Section: Slam Mapping Overviewmentioning

confidence: 99%

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

et al. 2022

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Simultaneous localization and mapping (SLAM) is a dual process responsible for the ability of a robotic vehicle to build a map of its surroundings and estimate its position on that map. This paper presents the novel concept of creating a 3D map based on the adaptive Monte-Carlo location (AMCL) and the extended Kalman filter (EKF). This approach is intended for inspection or rescue operations in a closed or isolated area where there is a risk to humans. The proposed solution uses particle filters together with data from on-board sensors to estimate the local position of the robot. Its global position is determined through the Rao–Blackwellized technique. The developed system was implemented on a wheeled mobile robot equipped with a sensing system consisting of a laser scanner (LIDAR) and an inertial measurement unit (IMU), and was tested in the real conditions of an underground mine. One of the contributions of this work is to propose a low-complexity and low-cost solution to real-time 3D-map creation. The conducted experimental trials confirmed that the performance of the three-dimensional mapping was characterized by high accuracy and usefulness for recognition and inspection tasks in an unknown industrial environment.

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Section: Slam Mapping Overviewmentioning

confidence: 99%

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

et al. 2022

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“…Multiple indoor mapping results have presented in the work. A comprehensive review has been published on multiple robot map merging procedures [19]. The authors presented state of the art research works by categorizing them as per type of maps generated and adoptability of the algorithm chosen for map merging.…”

Section: Related Workmentioning

confidence: 99%

“…Now using the distinct recorded pose (xn,yn,θn) at respective time, the accumulated scan SC can be transformed into the SLAM referenced system to gradually provide a reconcilable 3D point cloud map. The overall transformation of the scan SC into these SLAM coordinated frame have shown in (4).…”

Section: A Single Rover Based 3d Map Developmentmentioning

confidence: 99%

“…Many research groups have used different kind of rovers equipped with a variety of laser scanners for developing the map model of the targeted region. The complexity arises when multiple rovers provide individual mapping results of the portions of the environment they explored and the final map of the environment needs to generate by knowing the correct transformations of all individual maps [4]. The task of determining respective transformations of each rover has been addressed by different researchers through centralized or distributed computational algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Rover Team Based 3D Map Building Using Low Cost 2D Laser Scanners

2022

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This paper presents a solution to develop 3D map of indoor places using a team of rovers equipped with low cost 2D laser scanners. An orthogonal combination of two RPLidar 2D laser scanners has been used on every rover to build 3D point cloud map of the explored regions. Each rover of the team has been distantly placed inside the surveying environment in order to independently scan and built the unique 3D point cloud map of the region. The interfacing of scanners and the processing of simultaneous localization and mapping (SLAM) technique for each moving rover have been achieved using Robot Operating System (ROS). The individually build 3D maps have been merged in offline mode through sensor fusion application using Kalman Filter (KF) technique. Different indoor vicinities have been tested and complete 3D point cloud maps have been established which have been found accurate when compared to ground truths. The developed point cloud map has been further utilized to establish Building Information Model (BIM) and found valid for the surveyed region. In comparison to existing surveying and scanning solutions of the regional market, the presented method has been found precise, quick and convenient to provide structural details of surveyed entities at highly affordable rates.INDEX TERMS 2D laser scanner, map merging, ROS, rover, SLAM.

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“…Regarding the map construction of SLAM, there are three commonly used maps [42]: Occupation grid, Feature-based map and Topological map.…”

Section: Introductionmentioning

confidence: 99%

Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar

Ismail

Roy

Sheu

et al. 2022

Sensors

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This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored floor of a building. We had implemented the e-SLAM (exploration-based SLAM) using the coordinate transformation and the navigation prediction techniques to achieve that purpose in the engineering school building which consists of many 100-m2 labs, corridors, elevator waiting space and the lobby. We first derive the LiDAR mesh for the orthogonal walls and filter out the static furniture and dynamic humans in the same space as the IMR. Then, we define the LiDAR pose frame including the translation and rotation from the orthogonal walls. According to the MSC (most significant corner) obtained from the intersection of the orthogonal walls, we calculate the displacement of the IMR. The orientation of the IMR is calculated from the alignment of orthogonal walls in the consecutive LiDAR pose frames, which is also assisted by the LQE (linear quadratic estimation) method. All the computation can be done in a single processor machine in real-time. The e-SLAM technique leads to a potential for the in-house service robot to start operation without having pre-scan LiDAR maps, which can save the installation time of the service robot. In this study, we use only the LiDAR and compared our result with the IMU to verify the consistency between the two navigation sensors in the experiments. The scenario of the experiment consists of rooms, corridors, elevators, and the lobby, which is common to most office buildings.

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A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Cited by 38 publications

References 107 publications

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

A Rover Team Based 3D Map Building Using Low Cost 2D Laser Scanners

Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar

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