UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Xiao-qin, Liu; Li, Xiang; Shi, Qi; Xu, Chuanpei; Tang, Yanmei

doi:10.1177/15501477211009814

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…In our approach, we are interested in traditional optical flow-based methods that do not use deep learning [25][26][27]. These techniques have already been applied to flying robots for ego-motion estimation [28], path planning [29], and attitude estimation [30], among other uses. In addition, optical flow shows excellent results in mid-air collision avoidance [29,[31][32][33][34].…”

Section: Related Workmentioning

confidence: 99%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

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Section: Related Workmentioning

confidence: 99%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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“…In [6] in the authors proposed improved motion compensation using feature block selection, look-ahead rotation, fault case detection and filtering using PX4FLOW hardware by tunning the camera resolution, the interval of adaptive boxes and advanced search algorithm. In [7] presents a UAV position estimation using an optical flow approach using a Gated Recurrent Unit (GRU) network-based pointing angles and Magnetic, Angular Rate, and Gravity (MARG) sensors improving robustness and performance in real-time experimentation.…”

Section: Introductionmentioning

confidence: 99%

Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment

Flores,

Gonzalez-Hernandez,

Salazar

et al. 2024

Sensors

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This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms.

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“…Orientation estimation is to estimate the parameters to describe the angular position quantitatively. And it has been widely used in many fields such as motion tracking (Baldi et al , 2019; Whle and Gebhard, 2020; Yuan et al , 2019), unmanned aerial vehicle (Youn, 2020; Shi et al , 2016; Liu et al , 2021), medical treatment (Salchow and Christina, 2019; Connolly et al , 2018) and steerable drilling (Geng et al , 2020; Xue et al , 2016). Currently, triaxial accelerometers, triaxial magnetic sensors and triaxial gyros are the most widely used sensors for orientation estimation (Liu et al , 2021; Dai and Jing, 2021; Wu, 2020; Wilson et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

“…And it has been widely used in many fields such as motion tracking (Baldi et al , 2019; Whle and Gebhard, 2020; Yuan et al , 2019), unmanned aerial vehicle (Youn, 2020; Shi et al , 2016; Liu et al , 2021), medical treatment (Salchow and Christina, 2019; Connolly et al , 2018) and steerable drilling (Geng et al , 2020; Xue et al , 2016). Currently, triaxial accelerometers, triaxial magnetic sensors and triaxial gyros are the most widely used sensors for orientation estimation (Liu et al , 2021; Dai and Jing, 2021; Wu, 2020; Wilson et al , 2019). In applications, these sensors are mounted on target objects, and through the alignment and correction operations (Li et al , 2018; Wang et al , 2019), the sensor frames can be regarded as coinciding with the object frame.…”

Section: Introductionmentioning

confidence: 99%

Orientation estimation using vector observations with one or two components lost

Shi

Shang²

2022

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Purpose Traditional algorithms require at least two complete vector observations to estimate orientation parameters. However, sensor faults and disturbances may cause some components of vector observations unavailable. This paper aims to propose algorithms to realize orientation estimation using vector observations with one or two components lost. Design/methodology/approach The fundamental of the proposed method is using norm equation and dot product equation to estimate the lost components, then, using an improved TRIAD to calculate attitude matrix. Specific algorithms for one and two lost components cases are constructed respectively, and the nonuniqueness of orientation estimation is analyzed from a geometric point of view. At last, experiments are performed to test the proposed algorithms. Findings The loss of components results in the loss of orientation information. The introduction of the norm equation and dot product equation can partially compensate for the loss of information. Experiment results and analysis show that the proposed algorithms can provide effective orientation estimation, and in vast majority of applications, the proposed algorithms can provide a unique solution in one lost component case and double solutions in two lost components case. Originality/value The proposed method addresses the problem of orientation estimation when one or two components of vector observations are unavailable. The introduction of the norm equation and dot product equation makes the calculation cost low, while the analyses from a geometric point of view makes the study of nonuniqueness more intuitive.

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UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Cited by 12 publications

References 30 publications

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment

Orientation estimation using vector observations with one or two components lost

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