Towards Simulating Semantic Onboard UAV Navigation

Mandel, Nicolas; Alvarez, Fernando Vanegas; Milford, Michael; Gonzalez, Felipé

doi:10.1109/aero47225.2020.9172771

Cited by 4 publications

(9 citation statements)

References 31 publications

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“…Time behavior is not evaluated due to its inherent issues discussed in the literature [21,22]. GPU usage has been broadly evaluated in the literature [23,26,27], and as the evaluated application does not target it [35], it is omitted. Capacity is deemed not adequate to evaluate, because the system load only changes minutely over time and inputs cannot be scaled infinitely.…”

Section: Performance Efficiency 2 Compatibility 3 Reliabilitymentioning

confidence: 99%

“…A UAV in a controlled environment with a suspended arm [29,30] 3. An additional module on a UAV to improve performance by integrating semantics into its navigation [35] These cases lead to assumptions concerning the assignment of weights, which require revision until the matrices can be considered consistent as defined by Equation (1). The weight matrices are explained in detail in Section 4.5.…”

Section: A Fully Autonomous Uav Performing Oil Rig Inspection [5]mentioning

confidence: 99%

“…This work is targeting the HITL development step shown in Figure 5. Algorithm development, numerical simulations, and SITL-shown in Figure 3-were conducted in previous research and the performance of the developed package was verified [35]. The current work focuses on translating the simulation with the package into a distributed set-up, with the simulation running on a host PC-connected via Ethernet to the router-and parts of the package-in variations shown in Figure 1-running on the embedded system.…”

Section: Experimental Designmentioning

confidence: 99%

“…In this work, we demonstrate the methods using a software-in-the-loop (SITL) ROS package developed and validated by the authors [35]. Figure 3 shows the simulation in the process.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a Monte Carlo (MC) simulation is used over the admissible input space, which allows to compare the effects of different relative weights to give preference to another alternative. Figure 1 presents one example of the differences between an onboard and a real-time system in the context of UAV onboard decision-making [35]. The split of the nodes is dependent on the frequency and the degree of closeness to hardware required by hardware components.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Mandel

Milford

Gonzalez

2020

Sensors

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The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.

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Section: Performance Efficiency 2 Compatibility 3 Reliabilitymentioning

confidence: 99%

Section: A Fully Autonomous Uav Performing Oil Rig Inspection [5]mentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

“…In this work, we demonstrate the methods using a software-in-the-loop (SITL) ROS package developed and validated by the authors [35]. Figure 3 shows the simulation in the process.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Mandel

Milford

Gonzalez

2020

Sensors

Self Cite

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Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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