Tool for 3D Gazebo Map Construction from Arbitrary Images and Laser Scans

Lavrenov, Roman; Zakiev, Aufar

doi:10.1109/dese.2017.33

Cited by 18 publications

(8 citation statements)

References 13 publications

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“…2) Height-map based automatic 3D world construction in Gazebo: [30] presents a method to automate the process of constructing a 3D virtual environment (world) in Gazebo from a 2D map that is built using real sensor data. The map is represented in the form of an occupancy grid, where a white pixel represents a free space, black an occupied space and gray represents a space that is not clearly defined.…”

Section: A Simulators For 3d Environment Robot and Sensor Modellingmentioning

confidence: 99%

“…Abbyasov et al [31] validated the efficacy of the tool by using it to create virtual environments in Gazebo and using them to test various navigation algorithms. The authors mention that the LIRS-WCT is more efficient than the approach in [30] because of its added features such as scalability and it's relatively higher performance measured in terms of the real time factor in Gazebo.…”

Section: A Simulators For 3d Environment Robot and Sensor Modellingmentioning

confidence: 99%

“…6) Absence of ambient sound modelling in the virtual scene: Additionally, some robots use environmental sound or human speech for navigation [60], [61], [62], and [63]. Currently, there is no simulator for testing sound based navigation algorithms.. 7) Lack of realistic modelling simulation: Although some of the simulators such as Gazebo allow one to model sensor noise for various sensors such as for lidar and camera [64], [30], there are many types of noises that currently cannot be modelled in simulation. This includes ambient noise for the acoustic input based navigation algorithms [65] or a noise originating from the robot itself through its motors and fans [66].…”

Section: Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Simulators for Mobile Social Robots:State-of-the-Art and Challenges

Kaur¹,

Liu²,

Shi³

2022

Preprint

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The future robots are expected to work in a shared physical space with humans [1], however, the presence of humans leads to a dynamic environment that is challenging for mobile robots to navigate. The path planning algorithms designed to navigate a collision free path in complex human environments are often tested in real environments due to the lack of simulation frameworks. This paper identifies key requirements for an ideal simulator for this task, evaluates existing simulation frameworks and most importantly, it identifies the challenges and limitations of the existing simulation techniques.First and foremost, we recognize that the simulators needed for the purpose of testing mobile robots designed for human environments are unique as they must model realistic pedestrian behavior in addition to the modelling of mobile robots. Our study finds that Pedsim ros [2] and a more recent SocNavBench framework [3] are the only two 3D simulation frameworks that meet most of the key requirements defined in our paper. In summary, we identify the need for developing more simulators that offer an ability to create realistic 3D pedestrian rich virtual environments along with the flexibility of designing complex robots and their sensor models from scratch.

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Section: A Simulators For 3d Environment Robot and Sensor Modellingmentioning

confidence: 99%

Section: A Simulators For 3d Environment Robot and Sensor Modellingmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

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Simulators for Mobile Social Robots:State-of-the-Art and Challenges

Kaur¹,

Liu²,

Shi³

2022

Preprint

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“…For vSLAM performance evaluation we constructed a virtual Gazebo world 26,27 that contains the modified collapsed police station 28 model, which was extended with additional objects, including pieces of walls, trusses and furniture (Fig. 1, left).…”

Section: Experimental Configurationmentioning

confidence: 99%

Comparative analysis of ROS-based centralized methods for conducting collaborative monocular visual SLAM using a pair of UAVs

Rivalevich¹,

Grigorevna²,

Edgar³

et al. 2020

Robots in Human Life

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robo-server.uacj.mx Unmanned Aerial Vehicle (UAV) is a flying robot that acts without a constant human pilot involvement. UAVs are applied in military and civilian areas, in search and rescue operations, 3D mapping, simultaneous localization and mapping (SLAM) and other tasks. SLAM approaches are based on various sensors usage including lidars and cameras. Visual SLAM approaches rely on visual sensing systems and successfully operate within GPS-denied environments. Further, applying several UAVs allows for complex tasks that cannot be handled by a single robot, minimizes exploration time and adds a security level for a case of a single robot failure. This paper presents a comparison of two most applicable vision-based collaborative monocular SLAM methods in Robot operating system, CORB-SLAM and CCM-SLAM, that run on a pair of UAVs. The evaluation is performed on preassembled datasets that correspond to a virtual environment in the Gazebo simulator. The error estimation in virtual experiments demonstrated that CCM-SLAM has a higher global localization accuracy than CORB-SLAM.

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“…Since for full-size autonomous vehicles safety is critical, typically all control gestures are predefined in advance and then the robot control system is taught to recognize these gestures using an exhaustive set of examples [35]. However, such approach requires an operator to carefully study the gestures and to be always concentrated in order to use a proper one.…”

Section: Introductionmentioning

confidence: 99%

Pilot Studies on Avrora Unior Car-Like Robot Control Using Gestures

Nikiforov

Tsoy

Safin

et al. 2021

Smart Innovation, Systems and Technologies

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Gesture recognition is not only an important communication channel in human-human interaction but it also allows a human to communicate with other intelligent devices. This paper presents a concept for controlling the car-like robot Avrora Unior locomotion using gestures. We created a list of 18 control commands that contains basic and compound commands. A group of 17 volunteers used this list to create individual control gestures independently. A small part of the obtained dataset of gestures was used with the Teachable machine service in order to preliminary evaluate a possibility of constructing a full-scale model and to train it appropriately.

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Tool for 3D Gazebo Map Construction from Arbitrary Images and Laser Scans

Cited by 18 publications

References 13 publications

Simulators for Mobile Social Robots:State-of-the-Art and Challenges

Simulators for Mobile Social Robots:State-of-the-Art and Challenges

Comparative analysis of ROS-based centralized methods for conducting collaborative monocular visual SLAM using a pair of UAVs

Pilot Studies on Avrora Unior Car-Like Robot Control Using Gestures

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