Optical flow sensor integrated navigation system for quadrotor in GPS-denied environment

Tsai, Shang-En; Zhuang, Shih-Hsien

doi:10.1109/icrae.2016.7738795

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…This approach is available only outdoors since the system uses GPS measurements. In a hybrid case, Tsai and Zhuang [15] adapted optical flow and ultrasonic sensors on the drone and achieved better results than GPS positioning. A similar approach was implemented for real-time pose estimation by Hong et al [16], combining the Cubature Kalman Filter on IMU readings with an optical-flow-based correction to minimize positioning error.…”

Section: Trackingmentioning

confidence: 99%

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Konstantoudakis

Christaki

Tsiakmakis

et al. 2022

Drones

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Traditional drone handheld remote controllers, although well-established and widely used, are not a particularly intuitive control method. At the same time, drone pilots normally watch the drone video feed on a smartphone or another small screen attached to the remote. This forces them to constantly shift their visual focus from the drone to the screen and vice-versa. This can be an eye-and-mind-tiring and stressful experience, as the eyes constantly change focus and the mind struggles to merge two different points of view. This paper presents a solution based on Microsoft’s HoloLens 2 headset that leverages augmented reality and gesture recognition to make drone piloting easier, more comfortable, and more intuitive. It describes a system for single-handed gesture control that can achieve all maneuvers possible with a traditional remote, including complex motions; a method for tracking a real drone in AR to improve flying beyond line of sight or at distances where the physical drone is hard to see; and the option to display the drone’s live video feed in AR, either in first-person-view mode or in context with the environment.

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Section: Trackingmentioning

confidence: 99%

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Konstantoudakis

Christaki

Tsiakmakis

et al. 2022

Drones

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“…In [2], the concept of image moments is utilized from environment images to estimate the translational velocity of the UAV reliably. A combination of optical flow and ultrasound sensors has been used in [3] to achieve a stable indoor hovering 1 Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India. Email: (nishanthrao, vssuresh, varuncr)@iisc.ac.in performance of a micro aerial vehicle.…”

Section: Introductionmentioning

confidence: 99%

Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-denied operation of Micro-UAV

Rao,

Sundaram,

Raghavendra

2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to the global coordinate frame (GPS), which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass, etc., using the onboard extended Kalman filter (EKF) to estimate the states of the UAV. The experimental flight data collected from an RTK-GPS facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to fuse the predicted data using the SN-MNN, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.

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“…Optical flow is one of the VO approaches, which is based on the bird and insects flight navigation [ 34 ]. The optical flow sensors are typically employed for platform velocity estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The optical flow sensors are typically employed for platform velocity estimation. Previous researches integrated VO with INS [ 35 ], and/or magnetometers [ 34 ], or fused with motion constraints to aid INS [ 16 ]. VO can be integrated with maps, for example; [ 36 ] proposed a geo-localization land vehicle system based on visual odometry and public domain map sources for GNSS denied environment.…”

Section: Introductionmentioning

confidence: 99%

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

Moussa

Zahran

Mostafa

et al. 2020

Sensors

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Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.

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Optical flow sensor integrated navigation system for quadrotor in GPS-denied environment

Cited by 14 publications

References 6 publications

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Drone Control in AR: An Intuitive System for Single-Handed Gesture Control, Drone Tracking, and Contextualized Camera Feed Visualization in Augmented Reality

Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-denied operation of Micro-UAV

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

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