Development of task-oriented ROS-based Autonomous UGV with 3D Object Detection

Raveendran, Rajesh; Ariram, Siva; Tikanmäki, Antti; Röning, Juha

doi:10.1109/rcar49640.2020.9303034

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Object detectors in this specific area are the You Only Look Once model (YOLO) and the Single Shot MultiBox Detector (SSD). Three applications used the YOLOv3 [48][49][50]. YOLOv3 shows great adaptability achieving very high performance.…”

Section: Software/methodsmentioning

confidence: 99%

“…In addition, the DroneDeploy [54] and the PIX4D [27] as drone mapping software were used to assist the fire detection procedure. Finally, the open-source Robot Operating System (ROS) [19,49] for drone navigation as well as the Node-Red [29] for programming event-driven applications were also selected.…”

Section: Software/methodsmentioning

confidence: 99%

“…The Gray Level Co-occurrence Matrix (GLCM) is able to perform texture analysis and extract the features from images [53]. Furthermore, other methods include the Spatial and Geometric Histograms (SGH) descriptor, which is a feature descriptor for three-dimensional (3D) local surface [54,55], the visual descriptor Local Binary Pattern (LBP) [48], which is a texture operator for image classification [49] or even a novel method the Forest Fire Detection Index (FFDI) [56] which was developed first by Henry Cruz, Martina Eckert, Juan Meneses and José-Fernán Martínez [57].…”

Section: Software/methodsmentioning

confidence: 99%

See 2 more Smart Citations

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Section: Software/methodsmentioning

confidence: 99%

Section: Software/methodsmentioning

confidence: 99%

Section: Software/methodsmentioning

confidence: 99%

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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“…Additional color information is used to detect and localize the bricks. A UGV for Challenge 3 was developed by (Raveendran et al, 2020). Again, a 3D LiDAR sensor is used for SLAM.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Wall-building and Firefighting: Team NimbRo’s UGV Solution for MBZIRC 2020

Lenz¹,

Quenzel²,

Periyasamy³

et al. 2022

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Autonomous robotic systems for various applications including transport, mobile manipulation, and disaster response are becoming more and more complex. Evaluating and analyzing such systems is challenging. Robotic competitions are designed to benchmark complete robotic systems on complex state-of-the-art tasks. Participants compete in defined scenarios under equal conditions. We present our UGV solution developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Our hardware and software components to address the challenge tasks of wall-building and firefighting are integrated into a fully autonomous system. The robot consists of a wheeled, omnidirectional base, a 6 DoF manipulator arm equipped with a magnetic gripper, a highly efficient storage system to transport box-shaped objects, and a water-spraying system to extinguish fires. The robot perceives its environment using 3D LiDAR, as well as RGB and thermal camera-based perception modules and is capable of picking box-shaped objects and constructing a pre-defined wall structure. Its sensor modules also facilitate detecting and localizing heat sources to extinguish potential fires. A high-level planner coordinates and applies the robot’s skills to complete the Challenge tasks. We analyze and discuss our successful participation during the MBZIRC 2020 finals, present further experiments, and provide insights to our lessons learned.

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“…Deep learning object detection algorithms such as You Only Look Once (YOLO), Single Shot Detector (SSD), Faster R-CNN etc have been used in many robotic applications. In [4,5,6,7] object detection using pre-trained Convolutional Neural Networks (CNNs) integrated with visual SLAM to enhance the robotic capabilities. In this paper, three commonly used Lidar-based SLAM algorithms are evaluated in simulation environment.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation of Indoor Service Robot with Object Recognition using Deep Learning

Abhijith¹,

S²

2022

IJRASET

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Indoor Service robots are becoming popular day by day. The task of service robot is to perform some predefined tasks originally done by human in houses, schools, offices etc. This paper proposes an autonomous indoor service robot system integrated with multi-sensors such as Lidar and Kinect for the smooth functioning of indoor service robots. Firstly, a map of the environment is created using Simultaneous Localization and Mapping (SLAM), a commonly used technique for creating map and positioning the robot at the same. In this paper different Lidar-based SLAM are compared and the best SLAM is chosen for creating 3D map for this system. For autonomous navigation, path planning plays a key role for fulfilling the task. A* algorithm is incorporated with this system to plan a global path. But global path planning algorithm fails to complete its task if any new or dynamic objects come to its path of navigation. To overcome such situations, a local path planning algorithm called Dynamic Window Approach (DWA) is added to this system. Another inevitable element in autonomous navigation is localization of robots in the map. Adaptive Monte Carlo Localization (AMCL) is the technique applied in the proposed system to localize the robot for the effective navigation of indoor service robot. To enhance the performance of service robot, YOLOv5 model is integrated with this system for real time object detection. The entire system is simulated in an open-source framework called Robot Operating System (ROS).

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Development of task-oriented ROS-based Autonomous UGV with 3D Object Detection

Cited by 7 publications

References 12 publications

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Autonomous Wall-building and Firefighting: Team NimbRo’s UGV Solution for MBZIRC 2020

Autonomous Navigation of Indoor Service Robot with Object Recognition using Deep Learning

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