Temporal Prediction-Based Temporal Iterative Tracking and Parallel Motion Estimation for a 1-ms Rotation-Robust LK-Based Tracking System

Hu, Tingting; Fuchikami, Ryuji; Ikenaga, Takeshi

doi:10.1109/tim.2023.3295456

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…In Eq. (11), 𝑃 is the current robot_local_position odometer information observation value, 𝑃 is the previous time value, and during the testing process, the UAV is set to move at a constant speed, so 𝑉 = 𝑉 the speed remains unchanged:…”

Section: Optimization Of Position Information Output Using Kalman Fil...mentioning

confidence: 99%

“…However, in the application scenario of rapid movement of UAVs, coordinate system drift may occur. [10] Gao et al [10] proposed VIO (visual-inertial odometry) as the front-end algorithm for SLAM mapping in the application of quadruped robots, and processed the image data of binocular cameras through LK optical flow tracking [11] and pre-integrated IMU data [12], optimizing the close fusion of inertial sensor data and visual data, greatly improving positioning accuracy. However, in the case of rapid movement of UAVs, visual data noise is high, leading to inaccurate positioning of the odometer output by VIO tight coupling and a decrease in positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Xu,

Chen,

Chen

et al. 2024

J. meas. eng.

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Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

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Section: Optimization Of Position Information Output Using Kalman Fil...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Xu,

Chen,

Chen

et al. 2024

J. meas. eng.

View full text Add to dashboard Cite

show abstract

Temporal Clustering and Temporal Reference Based Specular Detection For 1-MS Visual Feedback System

Hu,

Fuchikami,

Nosaka

2024

2024 IEEE International Conference on Image Processing (ICIP)

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Spatial Constrained and Candidate Expanded Parallel Matching for Ultra-Low Delay Camera Pose Estimation

Sun,

Li,

et al. 2024

2024 8th International Conference on Imaging, Signal Processing and Communications (ICISPC)

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Temporal Prediction-Based Temporal Iterative Tracking and Parallel Motion Estimation for a 1-ms Rotation-Robust LK-Based Tracking System

Cited by 3 publications

References 31 publications

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Temporal Clustering and Temporal Reference Based Specular Detection For 1-MS Visual Feedback System

Spatial Constrained and Candidate Expanded Parallel Matching for Ultra-Low Delay Camera Pose Estimation

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