Comparison of Two SLAM Algorithms Provided by ROS (Robot Operating System)

Olalekan, Ayoade Femi; Sagor, Jane Alam; Hasan, Hasibul; Oluwatobi, Adekunle Samuel

doi:10.1109/incet51464.2021.9456164

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…In the experiment, the charging piles and the robot can communicate with each other. In the real-world scenario, such a configuration can be easily established using ROS [35] (robot operating system), which provides the communication between robots. We tested the model and algorithm in five different tests.…”

Section: Simulation Experiments and Discussion Figure 7 Experimental ...mentioning

confidence: 99%

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.INDEX TERMS Autonomous mobile robot, service robot, path planning, robot charging

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Section: Simulation Experiments and Discussion Figure 7 Experimental ...mentioning

confidence: 99%

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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“…In recent years, utilising open-source platforms such as ROS is common in the development of SLAM algorithms [7]. Studies such as [10,11,32,33] investigate into various SLAM algorithms available in ROS, assessing their performance metrics. For instance, [10] evaluates GMapping, Hector SLAM, and Karto SLAM using benchmark datasets, while [11] and [33] employ the structural similarity index measure (SSIM) to gauge map similarity and CPU efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…Studies such as [10,11,32,33] investigate into various SLAM algorithms available in ROS, assessing their performance metrics. For instance, [10] evaluates GMapping, Hector SLAM, and Karto SLAM using benchmark datasets, while [11] and [33] employ the structural similarity index measure (SSIM) to gauge map similarity and CPU efficiency. Notably, research indicates that Karto SLAM consistently demonstrates superior performance in terms of mapping accuracy and CPU efficiency [10][11].…”

Section: Related Workmentioning

confidence: 99%

ROS-based Multi-sensor Integrated Localization System for Cost-effective and Accurate Indoor Navigation System

Irwansyah,

Heryadi,

Dewi

et al. 2024

Preprint

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Accurate localization is essential for enabling intelligent autonomous navigation in indoor environments. While Global Navigation Satellite Systems (GNSS) provide efficient outdoor solutions, applications in indoor environments require alternative approaches in order to determine vehicle's global position. This study investigates a ROS-based multi-sensor integrated localization system utilizing wheel odometry, inertial measurement unit (IMU), and 2D Light Detection and Ranging (LiDAR) based simultaneous localization and mapping (SLAM) for cost-effective and accurate indoor autonomous vehicle (AV) navigation. The paper analyzes the limitations of wheel odometry and IMU, highlighting their susceptibility to errors. To address these limitations, the proposed system leverages LiDAR SLAM for real-time map generation and pose correction. The Karto SLAM package from robot operating system (ROS) is chosen due to its superior performance in previous research. Furthermore, the paper presents a low-cost prototype utilizing Raspberry Pi and Arduino Mega as the main controllers, demonstrating the feasibility of achieving accurate indoor localization with readily available hardware. This research contributes to the development of practical and cost-effective indoor navigation solutions for autonomous vehicles in wide range applications.

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“…In literature (Olalekan et al 2021), the researchers made a comparative study of two SLAM methods available in ROS environment, i.e. GMapping and HectorSLAM.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Lidar Odometry and Mapping Based on Reference Laser Scanning

Koszyk,

Łabędź,

Grzelka

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Simultaneous localization and mapping (SLAM) is essential for the robot to operate in an unknown, vast environment. LiDAR-based SLAM can be utilizable in environments where other sensors cannot deliver reliable measurements. Providing accurate map results requires particular attention due to deviations originating from the device. This study is aimed to assess LiDAR-based mapping quality in a vast environment. The measurements are conducted on a mobile platform. Accuracy of the map collected with the LeGO-LOAM method was performed by making a comparison to a map gathered with geodetic scanning using ICP. The results provided 60% of fitted points in a distance lower than 5 cm and 80% in a distance lower than 10 cm. The findings prove the mileage of the map created with this method for other tasks, including autonomous driving and semantic segmentation.

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Comparison of Two SLAM Algorithms Provided by ROS (Robot Operating System)

Cited by 10 publications

References 4 publications

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

ROS-based Multi-sensor Integrated Localization System for Cost-effective and Accurate Indoor Navigation System

Evaluation of Lidar Odometry and Mapping Based on Reference Laser Scanning

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