Visual SLAM for Autonomous Drone Landing on a Maritime Platform

Dutrannois, Thomas; Tien-Thanh, Nguyen,; Hamesse, Charles; Cubber, Geert De; Janssens, Bart

doi:10.1109/ismcr56534.2022.9950582

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Drones are widely used in maritime missions, but landing on the deck of a ship during maritime missions is a challenge because ships and land are different. The landing of drones on ships requires consideration of the relative motion between overtime and drones, and the space on the deck is small, making the overall situation more variable [9]. SLAM can provide some assistance in this regard.…”

Section: Application Of Slam In the Field Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

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The Application of SLAM in Life and Scientific Research

Song

2023

HSET

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This article mainly focuses on the application of SLAM in technology and provides a literature review on the following aspects: 1) The application of SLAM in automatic parking of automobiles. This includes using AVP-SLAM for high-precision automatic parking in outdoor parking lots using convolutional neural networks, and using MOFISLM for high-precision automatic parking when indoor GPS signals are poor. 2) The application of SLAM in robotics. It includes three aspects. The first is using the GMapping algorithm to construct and locate maps with high accuracy, low computational complexity, and high robustness, enabling robots to plan the shortest path in unknown environments. The second is to use ORB-SLAM2, which can handle more complex situations, to navigate medical institution robots to improve hospital efficiency and reduce disease transmission rates. The third is for robots that perform tasks in complex and unknown terrains. The improved version of LAMP1.0, LAMP2.0, which is suitable for complex underground environments, can achieve multi robot collaborative cooperation with high accuracy and robustness. 3) Application of SLAM in the field of unmanned aerial vehicles. This article mainly elaborates on four aspects. The first is to use visual SLAM to achieve collaborative work between ground robots and drones, and to utilize the high camera advantage of drones to quickly build maps for ground robots, improve accuracy, and save time. The second is to utilize the application of VSLAM on unmanned aerial vehicles to contribute to agriculture. The third is ORB-SLAM3 to provide rapid and high-precision landing on deck for unmanned aerial vehicles in maritime missions. The fourth is using SLAM to assist unmanned aerial vehicles in landing in emergency or position situations (such, when GPS signals are lost).

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Section: Application Of Slam In the Field Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…So the improved monocular ORB-SLAM3 is more suitable for this project. In the future, the main task of this project is to implement it into the actual system [9].…”

Section: Application Of Slam In the Field Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

The Application of SLAM in Life and Scientific Research

Song

2023

HSET

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Landing UAV on Moving Surface Vehicle: Visual Tracking and Motion Prediction of Landing Deck

Nguyen,

Crismer,

De Cubber

et al. 2024

2024 IEEE/SICE International Symposium on System Integration (SII)

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Quantitative Assessment of Drone Pilot Performance

Doroftei,

De Cubber,

Lo Bue

et al. 2024

Drones

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This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human-performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs.

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Visual SLAM for Autonomous Drone Landing on a Maritime Platform

Cited by 3 publications

References 18 publications

The Application of SLAM in Life and Scientific Research

The Application of SLAM in Life and Scientific Research

Landing UAV on Moving Surface Vehicle: Visual Tracking and Motion Prediction of Landing Deck

Quantitative Assessment of Drone Pilot Performance

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