Performance Evaluation of LiDAR Point Clouds towards Automated FOD Detection on Airport Aprons

Mund, Johannes; Zouhar, Alexander; Meyer, Lothar; Fricke, Hartmut; Rother, Carsten

doi:10.1145/2899361.2899370

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…To solve FOD detection problem, some effective algorithms are proposed recently [ 31 , 32 , 33 , 34 , 35 , 36 ]. The algorithms based on different sensors, such as actively scanning LiDAR system [ 31 ], mm-wave FMCW radar [ 34 ] and wideband 96 GHz Millimeter-Wave Radar [ 35 ], could achieve good results in different environments. A cosecan squared beam pattern in elevation and a pencil-beam pattern in azimuth, generated through folded reflectarray antenna (FRA) by phase only control, is analyzed to detect objects on ground [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Cao

Peng

Cai

et al. 2018

Sensors

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In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

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Section: Related Workmentioning

confidence: 99%

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Cao

Peng

Cai

et al. 2018

Sensors

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“…Clearly, machine learning or other algorithms can be developed to identify the types of the incoming airplanes captured by the LiDAR sensor network [9]. In addition, LiDAR as remote sensing technology, can also be used for monitoring the surroundings to detect and identify hazardous objects to improve the safety of apron operations (see [9,10] seem to adequate for aircraft model identification. Note that the scale is preserved in LiDAR data, making object identification easier.…”

Section: Limitations and Possible Applicationsmentioning

confidence: 99%

“…Clearly, machine learning or other algorithms can be developed to identify the types of the incoming airplanes captured by the LiDAR sensor network [9]. In addition, LiDAR as remote sensing technology, can also be used for monitoring the surroundings to detect and identify hazardous objects to improve the safety of apron operations (see [9,10]). Besides the possible applications presented above, the original intent of this effort was to develop a LiDAR-based prototype system to demonstrate the feasibility that airplanes can be identified and tracked in airport environment without any cooperation from the aircraft.…”

Section: Limitations and Possible Applicationsmentioning

confidence: 99%

“…Various researchers have already investigated the LiDAR technology for improving airport safety [9,10], and our earlier studies [11,12] were the first attempt to use LiDAR sensors to track aircraft ground movements. As the continuation of our previous work, in this study, we apply a more complex sensor network, rigorously address the sensor georeferencing, and for the first time, track airplanes during landings.…”

Section: Introductionmentioning

confidence: 99%

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Object Tracking with LiDAR: Monitoring Taxiing and Landing Aircraft

Koppanyi

Toth

2018

Applied Sciences

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Featured Application: Tracking aircraft during taxiing, landing and takeoffs at airports; acquiring statistical data for centerline deviation analysis and veer-off modeling as well as for airport design in general.Abstract: Mobile light detection and ranging (LiDAR) sensors used in car navigation and robotics, such as the Velodyne's VLP-16 and HDL-32E, allow for sensing the surroundings of the platform with high temporal resolution to detect obstacles, tracking objects and support path planning. This study investigates the feasibility of using LiDAR sensors for tracking taxiing or landing aircraft close to the ground to improve airport safety. A prototype system was developed and installed at an airfield to capture point clouds to monitor aircraft operations. One of the challenges of accurate object tracking using the Velodyne sensors is the relatively small vertical field of view (30 • , 41.3 • ) and angular resolution (1.33 • , 2 • ), resulting in a small number of points of the tracked object. The point density decreases with the object-sensor distance, and is already sparse at a moderate range of 30-40 m. The paper introduces our model-based tracking algorithms, including volume minimization and cube trajectories, to address the optimal estimation of object motion and tracking based on sparse point clouds. Using a network of sensors, multiple tests were conducted at an airport to assess the performance of the demonstration system and the algorithms developed. The investigation was focused on monitoring small aircraft moving on runways and taxiways, and the results indicate less than 0.7 m/s and 17 cm velocity and positioning accuracy achieved, respectively. Overall, based on our findings, this technology is promising not only for aircraft monitoring but for airport applications.

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“…While the FOD detection problem has been studied widely in the literature, almost all the studies are for open spaces, especially airports, where the presence of such debris is particularly detrimental. Recent examples of airport FOD detection methods include deep learning for standard optical cameras [15,16,17]; fractional Fourier transform [18], power spectrum features-based classification [19], cross section characteristics [20], line of sight visibility analysis [21], adaptive leakage cancellation [22], and variational mode decomposition [23] for frequency modulated continuous mm-wave radars; object minimal boundary extraction for infra-red cameras [24]; and, scan or point cloud processing for light detection and ranging (LiDAR) sensors [25,26].…”

Section: Introductionmentioning

confidence: 99%

Human-Assisted Robotic Detection of Foreign Object Debris Inside Confined Spaces of Marine Vessels Using Probabilistic Mapping

Wong¹,

Marquette²,

Bykov³

et al. 2022

Preprint

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Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distancedriven comparisons between the nominal and online maps for local outlier identification. Simulation trials show extremely high recall but low precision for the outlier identification method. The assistance of remote humans is, therefore, taken to deal with the precision problem by going over the close-up robot camera images of the outlier regions. An online survey is conducted to show the usefulness of this assistance process. Physical experiments are also reported on a GPU-enabled mobile robot platform inside a scaled-down, prototype tank to demonstrate the feasibility of the FOD detection system.

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Performance Evaluation of LiDAR Point Clouds towards Automated FOD Detection on Airport Aprons

Cited by 14 publications

References 16 publications

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Object Tracking with LiDAR: Monitoring Taxiing and Landing Aircraft

Human-Assisted Robotic Detection of Foreign Object Debris Inside Confined Spaces of Marine Vessels Using Probabilistic Mapping

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