1D-LRF Aided Visual-Inertial Odometry for High-Altitude MAV Flight

Hu, Jiaxin; Hu, Jun; Shen, Yunjun; Lang, Xiaoming; Zang, Bo; Huang, Guoquan; Mao, Yinian

doi:10.1109/icra46639.2022.9811757

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…and solve for the values of t x and t y from (11). Here, we use the non-negativity of t y to exclude the wrong solution of ( 24) and obtain the translation vector t m c .…”

Section: Improved Solution Scheme For the P3p Problemmentioning

confidence: 99%

“…where A ij stands for the algebraic cosine formula of r ij . So, the rotation matrix R m c can be solved from (11) and (25). Due to the accuracy limitations of the actual calculations, Schmidt orthogonalization of R m c is also required.…”

Section: Improved Solution Scheme For the P3p Problemmentioning

confidence: 99%

“…Currently, the acquisition of relative localization information between UAVs still relies heavily on the absolute position data of each UAV from the Global Navigation Satellite System (GNSS) [10]. In addition, similar problems exist with relative localization via motion capture systems, simultaneous localization and mapping (SLAM) [11,12], and ground-based ultra-wide band (UWB) localization systems [13]. They all need to first obtain their respective position coordinates in the same spatial coordinate system from external infrastructure or environmental information and then solve for the relative localization information based on this.…”

Section: Introductionmentioning

confidence: 99%

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Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Si,

Xu,

et al. 2023

Drones

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Swarming is one of the important trends in the development of small multi-rotor UAVs. The stable operation of UAV swarms and air-to-ground cooperative operations depend on precise relative position information within the swarm. Existing relative localization solutions mainly rely on passively received external information or expensive and complex sensors, which are not applicable to the application scenarios of small-rotor UAV swarms. Therefore, we develop a relative localization solution based on airborne monocular sensing data to directly realize real-time relative localization among UAVs. First, we apply the lightweight YOLOv8-pose target detection algorithm to realize the real-time detection of quadcopter UAVs and their rotor motors. Then, to improve the computational efficiency, we make full use of the geometric properties of UAVs to derive a more adaptable algorithm for solving the P3P problem. In order to solve the multi-solution problem when less than four motors are detected, we analytically propose a positive solution determination scheme based on reasonable attitude information. We also introduce the maximum weight of the motor-detection confidence into the calculation of relative localization position to further improve the accuracy. Finally, we conducted simulations and practical experiments on an experimental UAV. The experimental results verify the feasibility of the proposed scheme, in which the performance of the core algorithm is significantly improved over the classical algorithm. Our research provides viable solutions to free UAV swarms from external information dependence, apply them to complex environments, improve autonomous collaboration, and reduce costs.

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“…and solve for the values of t x and t y from (11). Here, we use the non-negativity of t y to exclude the wrong solution of ( 24) and obtain the translation vector t m c .…”

Section: Improved Solution Scheme For the P3p Problemmentioning

confidence: 99%

Section: Improved Solution Scheme For the P3p Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Si,

Xu,

et al. 2023

Drones

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show abstract

“…The method improves the distinctiveness and invariance of local feature descriptors by using a multilayer perceptron network for dimensionality reduction Simple network architectures perform better for learning-based descriptors, while complex architectures are more suitable for handcrafted descriptors / (Hu et al, 2023) Proposed a visual-inertial odometry system that fuses point-plane maps Enhanced localization accuracy and robustness through point-plane map fusion Performance influenced by map quality and completeness…”

Section: Visual Simultaneous Localization and Mapping With Dynamic Ob...mentioning

confidence: 99%

A review of visual SLAM with dynamic objects

Qin,

2023

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Purpose This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains. Design/methodology/approach In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM. Findings This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field. Originality/value To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

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“…For VIO applications, Delaune et al [25] proposed an RVIO system that leverages a range measurement update model to assign depth to features and eliminate scale drift. Hu et al [26] extended the RVIO method by fully exploiting distance measurements to constrain all coplanar features. Simultaneously, they conducted online extrinsic calibration between the LRF and camera.…”

Section: Range-visual-inertial Odometrymentioning

confidence: 99%

Range–Visual–Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization

Hao,

He,

Liu

et al. 2023

Drones

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In Global Navigation Satellite System (GNSS)-denied environments, image registration has emerged as a prominent approach to utilize visual information for estimating the position of Unmanned Aerial Vehicles (UAVs). However, traditional image-registration-based localization methods encounter limitations, such as strong dependence on the prior initial position information. In this paper, we propose a systematic method for UAV geo-localization. In particular, an efficient range–visual–inertial odometry (RVIO) is proposed to provide local tracking, which utilizes measurements from a 1D Laser Range Finder (LRF) to suppress scale drift in the odometry. To overcome the differences in seasons, lighting conditions, and other factors between satellite and UAV images, we propose an image-registration-based geo-localization method in a coarse-to-fine manner that utilizes the powerful representation ability of Convolutional Neural Networks (CNNs). Furthermore, to ensure the accuracy of global optimization, we propose an adaptive weight assignment method based on the evaluation of the quality of image-registration-based localization. The proposed method is extensively evaluated in both synthetic and real-world environments. The results demonstrate that the proposed method achieves global drift-free estimation, enabling UAVs to accurately localize themselves in GNSS-denied environments.

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1D-LRF Aided Visual-Inertial Odometry for High-Altitude MAV Flight

Cited by 7 publications

References 8 publications

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

A review of visual SLAM with dynamic objects

Range–Visual–Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization

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