mrgs: A Multi-Robot SLAM Framework for ROS with Efficient Information Sharing

Martins, Gonçalo; Portugal, David; Rocha, Rui P.

doi:10.1007/978-3-030-45956-7_3

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…This is due to the ROS having available software libraries and tools such as Gazebo simulation, ROS Visualizer (RViz), and rosbag that can help to build and test the robot applications. Furthermore, it has a software framework that makes it easy for users to develop modular code and implement it in the robot [ 5 ]. As for the hardware, the computer specification of AMD Ryzen 5 3600 GPU 6-core processor with 16 GB of RAM and Ubuntu 16.04 is used in this paper to run the software system.…”

Section: Methodsmentioning

confidence: 99%

“…The characteristics of the sensor influence the accuracy of the map, particularly the accuracy of the obstacle sensing location [ 4 ]. Using high-cost sensors such as SICK LIDAR and Hokuyu LIDAR can achieve high accuracy in map estimation [ 5 , 6 , 7 ]; using low-cost sensors can result in inadequate map quality [ 8 , 9 , 10 , 11 , 12 ]. As a result, ANN is proposed to be used in this project.…”

Section: Introductionmentioning

confidence: 99%

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Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor

et al. 2023

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Commonly, simultaneous localization and mapping (SLAM) algorithm is developed using high-end sensors. Alternatively, some researchers use low-end sensors due to the lower cost of the robot. However, the low-end sensor produces noisy sensor measurements that can affect the SLAM algorithm, which is prone to error. Therefore, in this paper, a SLAM algorithm, which is a Rao-Blackwellized particle filter (RBPF) integrated with artificial neural networks (ANN) sensor model, is introduced to improve the measurement accuracy of a low-end laser distance sensor (LDS) and subsequently improve the performance of SLAM. The RBPF integrated with the ANN sensor model is experimented with by using the Turtlebot3 mobile robot in simulation and real-world experiments. The experiment is validated by comparing the occupancy grid maps estimated by RBPF integrated with the ANN sensor model and RBPF without ANN. Both the results in simulation and real-world experiments show that the SLAM performance of RBPF integrated with the ANN sensor model is better than the RBPF without ANN. In the real-world experiment results, the performance of the occupied cells integrated with the ANN sensor model is increased by 107.59%. In conclusion, the SLAM algorithm integrated with the ANN sensor model is able to improve the accuracy of the map estimate for mobile robots using low-end LDS sensors.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor

et al. 2023

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A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment

et al. 2022

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Inquiring the robot operating system community on the state of adoption of the ROS 2 robotics middleware

Portugal,

Rocha,

Castilho

2024

Int J Intell Robot Appl

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The robot operating system (ROS) is a well-established and popular open-source robotics middleware used for rapid prototyping of robotic applications. However, ROS has several known weaknesses, such as lack of support for real-time systems and limitations in fully distributed deployments with multiple robots. To address these weaknesses, ROS underwent a major update and the first version of ROS 2 was released back in 2015, being lingeringly adopted by the community in recent years. Yet, long-term support for ROS 1 will come to an end on May 2025. As such, it is more important than ever to analyze and explore the features of ROS 2, and understand its technological readiness. This work focuses on the state of adoption of ROS 2. Specifically, the article presents a user experience questionnaire targeting the ROS community to understand the more eminent needs with respect to ROS 2, determine the current levels of adoption, and identify what is holding the community back from migrating their ROS 1 applications to ROS 2. Results with more than 100 participants from around the world show that the community is aware of ROS 2 and the middleware is addressing most user expectations. We unveil the main reasons given for not migrating to ROS 2, further understand the community’s views on ROS 2, and identify what is missing to anticipate ROS 1 users migrating to ROS 2 and make ROS 2 widely adopted. Moreover, we gather important impressions on the appropriateness of ROS 2 for multi-robot system use cases.

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mrgs: A Multi-Robot SLAM Framework for ROS with Efficient Information Sharing

Cited by 5 publications

References 35 publications

Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor

Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor

A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment

Inquiring the robot operating system community on the state of adoption of the ROS 2 robotics middleware

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