An End-to-End UAV Simulation Platform for Visual SLAM and Navigation

Chen, Shengyang; Zhou, Weifeng; Yang, An-Shik; Chen, Han; Li, Boyang; Wen, Chih-Yung

doi:10.3390/aerospace9020048

Cited by 26 publications

(10 citation statements)

References 35 publications

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“…Gazebo is a 3D simulation program for robots and uses a physics engine similar to reality, where drone movements or algorithms can be verified in a virtual environment. It also generates and outputs various sensor data such as IMU, LiDAR, and camera data [23]. In Gazebo, sensor data are obtained by simulating the IMU and Ouster's OS0-32 Li-DAR used in the experiments.…”

Section: Simulation Resultsmentioning

confidence: 99%

Observability-Driven Path Planning Design for Securing Three-Dimensional Navigation Performance of LiDAR SLAM

2023

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This paper presents an efficient method for securing navigation performance by suppressing divergence risk of LiDAR SLAM through a newly proposed geometric observability analysis in a three-dimensional point cloud map. For this, observability characteristics are introduced that quantitatively evaluate the quality of the geometric distribution of the features. To be specific, this study adapts a 3D geometric observability matrix and the associated condition number for developing numerical benefit. In an extensive application, we implemented path planning in which the enhanced SLAM performs smoothly based on the proposed method. Finally, to validate the performance of the proposed algorithm, a simulation study was performed using the high-fidelity Gazebo simulator, where the path planning strategy of a drone depending on navigation quality is demonstrated. Additionally, an indoor autonomous vehicle experimental result is presented to support the effectiveness of the proposed algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

Observability-Driven Path Planning Design for Securing Three-Dimensional Navigation Performance of LiDAR SLAM

2023

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“…Therefore, the PARROT model was chosen for further consideration. MATLAB platform and built-in UAV PARROT tool has a high degree of connectivity [8], and integrated simulation based on hardware allows you to perform a quick and realistic test of both aircraft and flight scheduling algorithms.…”

Section: Analysis Of Existing Hardware Platforms and Scenarios For Au...mentioning

confidence: 99%

“…Three types of tool kits have been applied in such scenarios: localization, mapping, and planning. The localization kit uses onboard sensor information, such as that provided by a stereo camera [8,9]. The used architecture gives an accurate localization result (error < 0.3 m).…”

Section: Analysis Of Existing Hardware Platforms and Scenarios For Au...mentioning

confidence: 99%

Autonomous flight insurance method of unmanned aerial vehicles Parot Mambo using semantic segmentation data

Naso

Pohudina²,

Pohudin³

et al. 2023

reks

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Autonomous navigation of unmanned aerial vehicles (UAVs) has become in the past decade an extremely attracting topic, also due to the increasing availability of affordable equipment and open-source control and processing software environments. This demand has also raised a strong interest in developing accessible experimental platforms to train engineering students in the rapidly evolving area of autonomous navigation. In this paper, we describe a platform based on low-cost off-the-shelf hardware that takes advantage of the Matlab/Simulink programming environment to tackle most of the problems related to UAV autonomous navigation. More specifically, the subject of this paper is the autonomous control of the flight of a small UAV, which must explore and patrol an indoor unknown environment. Objectives: to analyse the existing hardware platforms for autonomous flight indoors, choose a flight exploration scenario of unknown premises, to formalize the procedure for obtaining a model of knowledge for semantic classification of premises, to formalize obtaining distance to obstacles using data camera horizontally employment and building on its barrier map. Namely, we use the method of image segmentation based on the brightness threshold, a method of training the semantic segmentation network, and computer algorithms in probabilistic robotics for mobile robots. We consider both the case of navigation guided by structural visual information placed in the environment, e.g., contrast markers for flight (such as path marked by a red tape), and the case of navigation based on unstructured information such as recognizable objects or human gestures. Basing on preliminary tests, the most suitable method for autonomous in-door navigation is by using\ object classification and segmentation, so that the UAV gradually analyses the surrounding objects in the room and makes decisions on path planning. The result of our investigation is a method that is suitable to allow the autonomous flight of a UAV with a frontal video camera. Conclusions. The scientific novelty of the obtained results is as follows: we have improved the method of autonomous flight of small UAVs by using the semantic network model and determining the purpose of flight only at a given altitude to minimize the computational costs of limited autopilot capabilities for low-cost small UAV models. The results of our study can be further extended by means of a campaign of experiments in different environments.

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“…These applications involve both the interaction problem and the navigation problem, which in many cases involve complex control algorithms, whose performance and coordination must be evaluated before implementation on real platforms [13], [14]. The emulation under ideal conditions, therefore, allows having an additional design tool that guarantees time and design reduction throughout the whole process [15].…”

Section: Introductionmentioning

confidence: 99%

An E2ED-based Approach to Custom Robot Navigation and Localization

Moreno,

Paez,

Martinez

2022

IJACSA

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Simultaneous mapping and localization or SLAM is a basic strategy used with robots and autonomous vehicles to identify unknown environments. It is of great attention in robotics due to its importance in the development of motion planning schemes in unknown and dynamic environments, which are close to the real cases of application of a robot. This is why, in parallel with research, they are also important in specialized training processes in robotics. However, access to robotic platforms and laboratories is often complex and costly, with high demands on time and resources, particularly for small research centers. A more efficient and affordable approach to working with autonomous algorithms and motion planning schemes is often the use of the ROS-Gazebo simulator, which allows high integration with customized non-commercial robots, and the possibility of an end-to-end design (E2ED) solution. This research addresses this approach as a training and research strategy with our ARMOS TurtleBot robotic platform, creating an environment for working with navigation algorithms, in localization, mapping, and path planning tasks. This paper shows the integration of ROS into the ARMOS TurtleBot project, and the design of several subsystems based on ROS to improve the interaction in the development of service robot tasks. The project's source code is available to the research community.

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An End-to-End UAV Simulation Platform for Visual SLAM and Navigation

Cited by 26 publications

References 35 publications

Observability-Driven Path Planning Design for Securing Three-Dimensional Navigation Performance of LiDAR SLAM

Observability-Driven Path Planning Design for Securing Three-Dimensional Navigation Performance of LiDAR SLAM

Autonomous flight insurance method of unmanned aerial vehicles Parot Mambo using semantic segmentation data

An E2ED-based Approach to Custom Robot Navigation and Localization

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