Onboard plane-wise 3D mapping using super-pixels and stereo vision for autonomous flight of a hexacopter

Sanchez-Rodriguez, Jose-Pablo; Aceves-López, Alejandro; Martínez-Carranza, José; Flores-Wysocka, Gabriel

doi:10.1007/s11370-020-00312-z

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…There are a variety of remote sensors, but this paper is focused on the most common imaging sensors: multispectral, hyperspectral, thermal, and RGB (visible spectrum) cameras. Other sensors such as spectrometers [25,26], stereo cameras [27,28], light detection and range (LiDAR) [29,30], and sonar [31,32] move beyond the scope of this article and will not be discussed.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Martinez-Heredia

Gálvez

Colodro

et al. 2023

Drones

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Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.

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Section: Introductionmentioning

confidence: 99%

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Martinez-Heredia

Gálvez

Colodro

et al. 2023

Drones

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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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Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

Marelli

Meng

et al. 2022

Un. Sys.

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This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor environments. The goal of the problem is to maximize the detection probability within the minimum search time. We propose a two-stage strategy to achieve this goal. In the first stage, a greedy approach is used to define a set of grid cells with the aim of maximizing the detection probability. In the second stage, an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is minimized. Simulation results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

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Onboard plane-wise 3D mapping using super-pixels and stereo vision for autonomous flight of a hexacopter

Cited by 3 publications

References 44 publications

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

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

Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

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