Safe Landing Zones Detection for UAVs Using Deep Regression

Abdollahzadeh, Sakineh; Proulx, Pier-Luc; Állili, Madjid; Lapointe, Jean-François

doi:10.1109/crv55824.2022.00035

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The UAV's on-board extended Kalman filter considers the surrounding obstacles that occlude landing and calculates a landing approach path. A method based on a deep learning approach was presented in [4]. The proposed model was built on a semantic segmentation architecture.…”

Section: Related Workmentioning

confidence: 99%

“…fixed wing, multi-rotor, etc., and the flight scenario, e.g. indoor/outdoor, remote/populated area, safe landing might appear to be a complex problem, particularly in cases where a predefined fail-safe landing spot is proscribed [4]. Safe landing in crowded areas is a strenuous task due to the volatility of the environment, owed to multiple diverging parameters, such as moving people, variable terrain, camera moves, occlusions etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Embedded light-weight approach for safe landing in populated areas

Mitroudas¹,

Balaska²,

Psomoulis³

et al. 2023

Preprint

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Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedded light-weight approach for safe landing in populated areas

Mitroudas¹,

Balaska²,

Psomoulis³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Real-time safe validation of autonomous landing in populated areas: from virtual environments to Robot-In-The-Loop

Tovanche-Picon,

González-Trejo,

Flores-Abad

et al. 2024

Virtual Reality

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Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful integration of UAVs in populated environments. Nonetheless, validating autonomous landing in real scenarios is a challenging task with a high risk of injuring people. In this work, we propose a framework for safe real-time and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments and physics-based simulation. The proposed evaluation pipeline includes the use of Unreal graphics engine coupled with AirSim for realistic drone simulation to evaluate landing strategies. Then, Software-/Hardware-In-The-Loop can be used to test beforehand the performance of the algorithms. The final validation stage consists in a Robot-In-The-Loop evaluation strategy where a real drone must perform autonomous landing maneuvers in real-time, with an avatar drone in a virtual environment mimicking its behavior, while the detection algorithms run in the virtual environment (virtual reality to the robot). This method determines the safe landing areas based on computer vision and convolutional neural networks to avoid colliding with people in static and dynamic scenarios. To test the robustness of the algorithms in adversary conditions, different urban-like environments were implemented, including moving agents and different weather conditions. We also propose different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works on this challenging task, and analyze them through several randomized iterations. The proposed approach allowed us to safely validate the autonomous landing strategies, providing an evaluation pipeline, and a benchmark for comparison. An extensive evaluation showed a 99% success rate in static scenarios and 87% in dynamic cases, demonstrating that the use of autonomous landing algorithms considerably prevents accidents involving humans, facilitating the integration of drones in human-populated spaces, which may help to unleash the full potential of drones in urban environments. Besides, this type of development helps to increase the safety of drone operations, which would advance drone flight regulations and allow their use in closer proximity to humans.

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Guaranteeing Partial State Synchronization for UAV Platoon Under Message or UAV Loss

Chatterjee,

Dutta

2024

2024 International Conference on Computing, Networking and Communications (ICNC)

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Safe Landing Zones Detection for UAVs Using Deep Regression

Cited by 4 publications

References 15 publications

Embedded light-weight approach for safe landing in populated areas

Embedded light-weight approach for safe landing in populated areas

Real-time safe validation of autonomous landing in populated areas: from virtual environments to Robot-In-The-Loop

Guaranteeing Partial State Synchronization for UAV Platoon Under Message or UAV Loss

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