UAV Navigation with Computer Vision – Flight Testing a Novel Visual Odometry Technique

DePaola, Richard; Chimento, Charles; Anderson, Michael L.; Brink, Kevin; Willis, Andrew

doi:10.2514/6.2018-2102

Cited by 16 publications

(6 citation statements)

References 4 publications

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“…The uncertainty is estimated with a statistical perturbation approach based on observed RGBD camera measurement noise. Preliminary testing showed that the dynamically estimated covariance values were commensurate with experimentally measured errors in the odometry [14]. In this work, the technique is further refined, and its accuracy is validated through flight testing by comparing the vehicle's computed estimates to ground truth obtained from a motion capture system.…”

mentioning

confidence: 81%

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Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

Anderson

Brink

Willis

2019

Journal of Guidance, Control, and Dynamics

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Demand is growing for unmanned air vehicles (UAVs) with greater autonomy, including the ability to navigate without GPS information, such as indoors. In this work, a novel visual odometry algorithm is developed and flight tested. It uses sequential pairs of red, green, blue, depth (RGBD) camera images to estimate the UAV's change in position (delta pose), which can be used to aid a navigation filter. Unlike existing related techniques, it uses a novel perturbation approach to estimate the uncertainty of the odometry measurement dynamically in real time, a technique that is applicable to a wide range of sensor preprocessing tasks aimed at generating navigation-relevant measurements. Real-time estimates of the delta pose and its covariance allow these estimates to be efficiently fused with other sensors in a navigation filter. Indoor flight testing was performed with motion capture, which demonstrated that the odometry and covariance estimates are accurate when appropriately scaled. Flights also demonstrated the algorithm used in a navigation filter to improve a velocity estimate, which represents a significant improvement over the state of the art for RGBD odometry.

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confidence: 81%

“…In previous work [12][13][14], a similar approach resulted in underestimating the covariance. This is to be expected because the perturbed point clouds only include estimates of camera lens noise and do not account for other sources such as incorrect visual feature correspondences.…”

Section: Odometry Covariance Estimationmentioning

confidence: 94%

Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

Anderson

Brink

Willis

2019

Journal of Guidance, Control, and Dynamics

Self Cite

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“…Finally, for autonomous navigation of UAVs in indoor environments, there are several methods that research groups have been working on, including: vision-based models using different visual techniques such as visual odometry (VO), simultaneous localization and mapping (SLAM), and optical flow technique [1], [32]- [39]. There are also a few research papers that showcase the use of deep neural networks in combination by visual techniques (e.g., [40]) or use of Li-DAR for autonomous flying (e.g., [41]).…”

Section: Related Workmentioning

confidence: 99%

“…Vision-based approaches are widely used for drone localization when GPS is not accessible (e.g., [1]). However, because of the vibration of the drone during flight, the accuracy of current vision-based methods is generally limited.…”

Section: Introductionmentioning

confidence: 99%

PILOT: High-Precision Indoor Localization for Autonomous Drones

Famili¹,

Stavrou²,

Wang³

et al. 2022

Preprint

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In many scenarios, unmanned aerial vehicles (UAVs), aka drones, need to have the capability of autonomous flying to carry out their mission successfully. In order to allow these autonomous flights, drones need to know their location constantly. Then, based on the current position and the final destination, navigation commands will be generated and drones will be guided to their destination. Localization can be easily carried out in outdoor environments using GPS signals and drone inertial measurement units (IMUs). However, such an approach is not feasible in indoor environments or GPS-denied areas. In this paper, we propose a localization scheme for drones called PILOT (High-Precision Indoor Localization for Autonomous Drones) that is specifically designed for indoor environments. PILOT relies on ultrasonic acoustic signals to estimate the target drone's location. In order to have a precise final estimation of the drone's location, PILOT deploys a three-stage localization scheme. The first two stages provide robustness against the multi-path fading effect of indoor environments and mitigate the ranging error. Then, in the third stage, PILOT deploys a simple yet effective technique to reduce the localization error induced by the relative geometry between transmitters and receivers and significantly reduces the height estimation error. The performance of PILOT was assessed under different scenarios and the results indicate that PILOT achieves centimeter-level accuracy for three-dimensional localization of drones.

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“…In the absence of the GPS, vision-based methods are widely used for localization and navigation of drones (e.g., [4]). However, the accuracy of current vision-based approaches is usually limited due to the drone's vibration during flight.…”

Section: Introductionmentioning

confidence: 99%

iDROP: Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement

Famili¹,

Stavrou²,

Wang³

et al. 2022

Preprint

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Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then the navigation command signal would be generated and passed on to the controller unit of the drone. In this paper, we propose a localization scheme for drones called iDROP (Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement) that is specifically devised for GPSdenied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is due to two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision three-dimensional localization scheme for drones. iDROP employs a waveform that is robust against multi-path fading. Moreover, by placing beacons in optimal locations, it reduces the localization error induced by the relative geometry between the transmitters and the receiver.

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UAV Navigation with Computer Vision – Flight Testing a Novel Visual Odometry Technique

Cited by 16 publications

References 4 publications

Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

PILOT: High-Precision Indoor Localization for Autonomous Drones

iDROP: Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement

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