Camera-GPS-IMU sensor fusion for autonomous flying

Lee, Yongseok; Yoon, Jaemin; Yang, Hongtao; Kim, Changu; Lee, Dongjun

doi:10.1109/icufn.2016.7536988

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…It suggests that this approach can be considered a valid procedure, even if it seems uncommon. In the specific field of UAVs, it is possible to find different techniques in the recent literature, from the fusion of sensors [42] to the self-calibration [43,44]. For instance, in [45], the authors propose a novel method for monitoring a UAV with a custom-made positioning module that exploits a fusion algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

Avola

Cinque

Foresti

et al. 2023

Sensors

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During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight.

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Section: Related Workmentioning

confidence: 99%

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

Avola

Cinque

Foresti

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…UAV [1], [2], hand motion tracking [3]) Likewise, for the state estimation of legged robots, several studies have developed methods using visual-inertial information with leg kinematic information [4], [5], [6], which circumvent the drift issue of methods using only proprioceptive sensors (i.e IMU and encoder) [7], [8], [9]. These state estimation methods using visual-inertial measurements, however, are fragile during aggressive dynamic locomotions such as pronking gait.…”

Section: Introductionmentioning

confidence: 99%

Tunable Impact and Vibration Absorbing Neck for Robust Visual-Inertial State Estimation for Dynamic Legged Robots

Kim

Lee

2023

IEEE Robot. Autom. Lett.

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“…They can have fixed or rotary wings, and can fly either remotely via a ground control station or autonomously. In either case, the UAV is equipped with an optical or ranging sensor or a combination of both, a GPS to provide the position and velocity of the UAV, an inertial measurement unit (IMU) to determine its orientation, and an on-board computer to fuse the GPS and IMU observations in real time to navigate the vehicle along a pre-determined flight path (Lee et al, 2016). However, the restricted payload capacity of low-cost UAVs imposes constraints on the quality of their navigation systems and the sensors they can carry (Whitehead and Hugenholtz, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Uav Surveys With Different Configurations

Elaksher,

Sanjenis,

Velasco

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recently, developments in airborne sensors and easy-to-fly, reliable, low-cost commercial UAVs have opened a new era for precise aerial mapping. The restricted payload capacity of low-cost UAVs imposes constraints on the quality of their navigation systems and the sensors they can carry. Therefore, the quality of photogrammetric products generated from UAV surveys needs to be assessed. In this study, photogrammetric mapping principles are employed in collecting, processing, and analyzing optical images collected using two UAV surveying systems. The first system was a 3DR Iris+ drone flying at 25 meters above the ground. The second system was a homemade drone similar to Tuffwing flying at about 70 meters above the ground. Both systems carried a Canon PowerShot S100 using a fixed 5.2 focal length. About 30 ground points were surveyed with a TS02 total station and served as the ground truth for data evaluation. After the flights, data was proceeded and geospatial products including DEMs and orthophotos produced were evaluated. Different ground control configurations were examined and ground check-points were used to evaluate the final accuracy of the geospatial products. The comparison of the derived 3D information from captured data with ground measurements showed a high correlation between the accuracy of the 3D products and the sensor specification, flying altitude, as well as image layout. This was supported by comparisons between actual errors and theoretical positional precision based on flying height, photo scale and air base was conducted.

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Camera-GPS-IMU sensor fusion for autonomous flying

Cited by 7 publications

References 11 publications

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

Tunable Impact and Vibration Absorbing Neck for Robust Visual-Inertial State Estimation for Dynamic Legged Robots

Assessing the Accuracy of Uav Surveys With Different Configurations

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