Feature Matching and Deep Learning Models for Attitude Estimation on a Micro-Aerial Vehicle

Chumuang, Narumol; Farooq, Adil; Irfan, Muhammad; Aziz, Sumair; Qureshi, Moomal

doi:10.1109/icci54995.2022.9744178

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…In the realm of cyber-attacks and robotic applications, Chumuang et al Chumuang et al (2022) introduce novel neural network models that leverage deep learning techniques to tackle these challenges. They utilize feature matching algorithms and deep learning models, including CNN and RNN, to identify consistent features and predict quaternions for Micro Aerial Vehicles (MAVs).…”

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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“…Existing works in autonomous UAV exploration missions focus on a target-oriented approach to recognize objects of interest in an unknown environment and reach them efficiently [7], [8]. To emulate real-life SAR missions and allow comparative assessment of competing solutions under challenging conditions, several events were organized in the past [9], including the 2020 Mohamed Bin Zayed International Robotics Challenge 1 (MBZIRC) wherein multiple challenges were held, especially the challenge 3 targeted an urban firefighting scenario for cooperated aerial and ground vehicles to navigate, detect, approach, and extinguish multiple simulated fires around and inside a building [10].…”

Section: Introductionmentioning

confidence: 99%

UAV Autonomous Indoor Exploration and Mapping for SAR Missions: Reflections from the ICUAS 2022 Competition

Farooq

Souli

Laoudias

et al. 2022

2022 19th International Conference on Ubiquitous Robots (UR)

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The technological advancement in Unmanned Aerial Vehicles (UAVs) or drones and their deployment in real-life Search and Rescue (SAR) missions is imminent. We, therefore, present a perception-aware autonomous exploration framework aimed at performing vision-based target detection and collision avoidance with an Unmanned Aerial Vehicle (UAV). The UAV utilizes a depth camera for maneuvering and finding the target. The underlying indoor exploration approach considers autonomous collision-free navigation, as well as target detection with a ballistic ball payload delivery without a prior map. Moreover, the proposed method allows safe navigation in enclosed unknown areas congested with randomly positioned obstacles and target locations. Our underlined end-toend system architecture integrates the proposed exploration strategy. Extensive simulation experiments, using several Key Performance Indicators (KPIs), showcase the effectiveness of the proposed Robot Operating System (ROS) framework in a simulated Gazebo environment under various parameter settings.

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Feature Matching and Deep Learning Models for Attitude Estimation on a Micro-Aerial Vehicle

Cited by 9 publications

References 18 publications

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

UAV Autonomous Indoor Exploration and Mapping for SAR Missions: Reflections from the ICUAS 2022 Competition

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