Radar-Inertial State-Estimation for UAV Motion in Highly Agile Manoeuvres

Michalczyk, Jan; Schöffmann, Christian; Fornasier, Alessandro; Steinbrener, Jan; Weiß, Stephan

doi:10.1109/icuas54217.2022.9836130

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Landmark-based approaches for localization using radar can be found in [9], [10]. Static objects are extracted as peaks in the intensity image and used as landmarks by reducing noise and multipath reflections in [9].…”

Section: B Related Workmentioning

confidence: 99%

“…The multiple points are then matched. Specific corner reflectors and the distance measurements to them are utilized in [10]. Works such as, e.g., [26], model landmarks using tags which register the radar signal and return it and can possibly encode an identifier.…”

Section: B Related Workmentioning

confidence: 99%

“…A common setup for indoor localization involves environment sensors, e.g., cameras, lidar, or radar sensors, that are mounted on the mobile device (also called inside-out approach). Based on the environment sensors, either features of the surroundings can be matched with a global map [7] or relative measurements to landmarks [8]- [10] can be obtained for localization. A wide range of inside-out approaches based on cameras are available [11], in particular in many Virtual Reality (VR) systems [12].…”

Section: Introductionmentioning

confidence: 99%

“…Lidars [15] can provide high resolution point clouds, while radars [16], [17] on the other hand provide intensity images and can also detect range rate. Different possibilities for landmarks exist, e.g., radar corner reflectors [10] or special transponders [18] are available. A property of Frequency Modulated Continuous Wave (FMCW) radars is their ability to detect range, range rate (i.e., velocity in range direction), and angle-of-arrival.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Thormann,

Steuernagel,

Baum

2024

Preprint

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A novel concept for indoor self-localization based on relative position measurements to rotating artificial landmarks (with known positions) using short-range radar is proposed. This includes a complete processing pipeline for extracting distance and angle measurements from the raw radar data, which consists of a neural network for distance estimation, a basic angle-of-arrival estimator, and a particle filter for position tracking. Due to the ability of radar to measure range rate, i.e., the velocity in the direction of a detection, it is possible to robustly detect the landmarks by detecting and localizing their micro-Doppler pattern. This mean localization is possible even under difficult conditions (e.g., light changes). Experiments with a wheeled mobile robot and common office fans as landmarks demonstrate the effectiveness of the approach for indoor localization.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Thormann,

Steuernagel,

Baum

2024

Preprint

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“…To address this challenge, several positioning solutions have been developed, including Inertial Odometry (IO) [54], Visual-Inertial Odometry (VIO) [55], and Radar-Inertial Odometry (RIO) [56,57]. Among these, RIO, which integrates radar and IMU data, demonstrates promise in specific scenarios.…”

Section: Uav's Odometry and Navigationmentioning

confidence: 99%

Robust flight control and visual/radar-based indoor navigation for autonomous multicopters and their swarms

Jing

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I would like to express my gratitude to Professor Jose A. Martinez-Lorenzo, my esteemed PhD advisor. His innovative thinking, dedication, and ability to secure funding have been instrumental in supporting SICA-Lab students, including myself. Professor Martinez-Lorenzo's guidance and willingness to engage in discussions have been invaluable in solving research challenges throughout my PhD journey. Under his mentorship, I have developed creative thinking and independence as a researcher.I extend my heartfelt thanks to my colleagues at the SICA-Lab for their unwavering dedication and collaborative spirit. Their teamwork has greatly facilitated rapid and high-quality progress in our research endeavors.I am deeply grateful to Professor Rifat Sipahi and Doctor Juan Heredia-Juesas for their generous contributions as members of my PhD committee. Their time and assistance during my dissertation process have been truly appreciated.Furthermore, I would like to express my profound appreciation to my parents. Their boundless love and unwavering support have been my pillar of strength throughout my PhD journey. Their belief in my decisions has bolstered my confidence and determination in pursuing both shortterm and long-term career goals.

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A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Zhan,

Zhou,

Zhang

et al. 2024

Auton. Intell. Syst.

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Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

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Radar-Inertial State-Estimation for UAV Motion in Highly Agile Manoeuvres

Cited by 7 publications

References 22 publications

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Robust flight control and visual/radar-based indoor navigation for autonomous multicopters and their swarms

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

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