DEVICE AND METHOD FOR POSITIONING AN AIRCRAFT

Description

[0001] The invention relates to a device and a method for positioning an aircraft in a monitoring area of ​​an apron of an airport, according to the preamble of claim 1 and claim 11 respectively.

[0002] Various systems for positioning aircraft on an airport apron are known from the prior art, particularly for docking an aircraft with a passenger boarding bridge. Typically, an aircraft is detected using optoelectronic sensors, and the aircraft type is determined based on specific segments of the aircraft and their shape, such as the shape of the nose (radome), its distance from the ground, or the position and / or shape of the engines. Knowing the dimensions of the aircraft type and its position detected by the optoelectronic sensors, the aircraft can then be guided to a predetermined parking position, or the docking of the aircraft with the passenger boarding bridge can be controlled or assisted. For this purpose, the aircraft pilot can be supported, for example, by an airport docking guidance system.The airport docking guidance system consists of a display mounted within the pilot's field of vision to show information in the cockpit, and a LiDAR sensor that measures the aircraft's position relative to its parking position. In addition to measuring the aircraft's position relative to its parking position, the LiDAR sensor must also provide information that allows for the classification of the aircraft type based on the detected segments, as the parking position depends on the aircraft type.

[0003] DE 4301637C2 describes a method for docking an aircraft to a passenger boarding bridge of an airport building by locating and guiding the aircraft from a starting position to a passenger boarding bridge using a laser transmitter and a laser receiver device.

[0004] EP 2 109 065 B1 relates to a method for locating and identifying objects, in particular aircraft, at an aerodrome and for the safe and efficient docking of aircraft at such an aerodrome, whereby the identification is carried out in a two-stage process. First, an aircraft profile is captured and compared with known profiles, and in a second stage, aircraft components, such as an engine, are captured and selected as the basis for distinguishing between aircraft. The acquisition of the profiles and aircraft components is carried out using a laser distance measuring device.

[0005] US Patent 7,702,453 B2 discloses a method for guiding an aircraft to a holding position within an aircraft stand at an airport using a radio frequency (RF) signal from an RFID tag. A laser rangefinder can assist the method.

[0006] EP 1 015 313 ​​B1 discloses a docking system for airport terminals, with a positioning device as part of a gate operating system of an airport terminal, by means of which an aircraft can be guided into a parking position specified for its type. A video system detects the aircraft as it approaches the airport terminal, and the recorded data is compared with a database in which template data sets are stored for different aircraft types.

[0007] US Patent 2022 / 0066025 A1 describes an apron monitoring system comprising a display and a radar-based system, as well as additional systems selectable from laser-based and imaging systems. These systems work together as a combined system to improve the efficiency and safety of aircraft handling at airports.

[0008] EP 4 030 188 A1 relates to a device and a method for securing a surveillance area with at least one FMCW LiDAR sensor. The FMCW LiDAR sensor scans a plurality of measurement points in a surveillance area and generates measurement data from transmitted or reflected light from the measurement points. A control and evaluation unit evaluates the measurement data and generates a safety-related signal based on the evaluation. The measurement data includes radial velocities of the measurement points, and the control and evaluation unit is configured to segment the measurement points using the radial velocities and to group them into objects and / or object segments.

[0009] US Patent 2022 / 137227 A1 discloses a system and method for monitoring and controlling vehicles. It comprises sensors and communication modules installed in vehicles to acquire their position, speed, and other relevant data. The sensors may include a coherent lidar sensor, such as a frequency-modulated continuous wave (FMCW) lidar sensor.

[0010] EP 3 757 968 ​​A1 discloses an arrangement for detecting an aircraft within a detection range using a remote sensing system, for example, a LiDAR system. A controller determines the positions of the aircraft's exterior surfaces and compares them with the coordinates of the aircraft's parking position to ensure safe parking. In case of deviations, a warning signal can be issued to prevent accidents.

[0011] However, the image acquisition systems commonly used in the prior art for recording the aircraft, in particular laser scanners and camera systems, have disadvantages which will be discussed in more detail below.

[0012] Laser scanners or LiDAR (Light Detection and Ranging) sensors are mostly based on direct time-of-flight measurement. A light pulse is emitted by the sensor, reflected by an object, and detected again by the sensor. The sensor determines the travel time of the light pulse and uses the speed of light in the propagation medium (usually air) to estimate the distance between the sensor and the object. Since the phase of the electromagnetic wave is not taken into account, this is referred to as an incoherent measurement principle. In an incoherent measurement, it is necessary to generate pulses consisting of many photons in order to receive the reflected pulse with a sufficient signal-to-noise ratio. In industrial environments, the number of photons within a pulse is generally limited by eye protection requirements.Consequently, trade-offs arise between maximum range, minimum object reflection, integration time, and the signal-to-noise ratio requirements of the sensor system. Incoherent radiation at the same wavelength (ambient light) also directly affects the dynamic range of the light receiver. Examples of incoherent radiation at the same wavelength include the sun, similar sensor systems, or the identical sensor system via multipath propagation, i.e., unwanted reflections.

[0013] Known camera systems from the prior art are based on measurement principles such as stereoscopy or indirect time-of-flight measurement. In indirect time-of-flight measurement, the phase difference of an AMCW (Amplitude Modulated Continuous Wave) transmission signal and its time-delayed copy after reflection from an object is determined. The phase difference corresponds to the time of flight and can be converted into a distance value using the speed of light in the propagation medium. However, both stereoscopy and indirect time-of-flight measurement are not particularly robust against sunlight and, especially when using so-called flash illumination, in which the entire scene is illuminated at once, do not achieve the necessary range for the Airport Docking application.Overall, the accuracy of detecting the aircraft or relevant features such as engine position or the shape of the aircraft nose can be unsatisfactory, and the computational effort for image processing is usually high.

[0014] The object of the invention is therefore to improve a device and a method for positioning an aircraft in a monitoring area of ​​an apron of an airport.

[0015] This problem is solved by a device and a method for positioning an aircraft in a monitoring area of ​​an apron of an airport, according to claim 1 and 12 respectively.

[0016] A device for positioning an aircraft initially comprises at least one optoelectronic sensor for detecting the aircraft. This sensor emits light beams into a monitoring area on an airport apron. The light beams scan a multitude of measuring points within the monitoring area, and the sensor generates measurement data from the transmitted or reflected light emitted by these points. For scanning the monitoring area, the sensor may incorporate conventional beam deflection devices and, for example, be designed as a scanner with at least one movable deflection mirror.

[0017] A control and evaluation unit is set up to analyze the generated measurement data. This involves first segmenting the measurement points and then grouping them, at least partially, into segments of the aircraft. "At least partially" in this context means that some of the measurement points scanned within the monitored area may relate to objects that are not part of the aircraft, such as people or vehicles on the apron, or even the apron itself. Segmentation can be performed using known digital image processing or machine vision techniques, such as... Pixel-oriented methods in grayscale images using threshold methods, edge-oriented methods such as the Sobel or Laplace operator and gradient search, region-oriented methods such as "region-growing", "region-splitting", "pyramid linking" or "split and merge", model-based methods such as the Hough transform, or texture-oriented methods.

[0018] Furthermore, the term "range segmentation" refers to specific methods for segmenting three-dimensional datasets. Range segmentation is described, for example, in the following scientific publications: "Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation" (Bogoslavskyi et al., 2016 IEEE / RSJ International Conference on Intelligent Robots and Systems, DOI: 10.1109 / IROS.2016.7759050) "Laser-based segment classification using a mixture of bag-of-words". (Behley et al., 2013 IEEE / RSJ International Conference on Intelligent Robots and Systems, DOI: 10.1109 / IROS.2013.6696957) "On the segmentation of 3d lidar point clouds" (Douillard et al., 2011 IEEE International Conference on Robotics and Automation, DOI: 10.1109 / ICRA.2011.5979818)

[0019] Furthermore, machine learning algorithms can be used to detect segments of the aircraft or the entire aircraft. According to current scientific knowledge, so-called deep neural networks are used for this purpose. Examples of such methods are described in the following scientific publications: Guo, Yulan, et al. "Deep learning for 3d point clouds: A survey." IEEE transactions on pattern analysis and machine intelligence 43.12 (2020): 4338-4364. Lang, Alex H., et al. "Pointpillars: Fast encoders for object detection from point clouds." Proceedings of the IEEE / CVF conference on computer vision and pattern recognition. 2019. Yan, Yan, Yuxing Mao, and Bo Li. "Second: Sparsely embedded convolutional detection." Sensors 10 / 18 (2018): 3337.

[0020] Aircraft segments are understood to be, in particular, those parts of the aircraft that are characteristic of a specific aircraft type due to features such as shape and / or position, for example, engines, landing gear, cockpit windows, or tail assembly. The term "aircraft segment" can also include an outline of the aircraft, provided the entire aircraft is detected by the sensor.

[0021] The control and evaluation unit is therefore further configured to extract features from the segments, assign the segments to an aircraft type from a multitude of aircraft types based on these extracted features, and output positioning information for the aircraft based on the assigned aircraft type, such as a distance and / or direction to a predetermined, aircraft-type-specific parking position. To assign the segments to an aircraft type, the control and evaluation unit can be configured to receive information about aircraft-type-specific segments, for example, from a database. The database can be part of the device or the control and evaluation unit itself, part of the airport's IT infrastructure, or even reside in a cloud.

[0022] According to the invention, the optoelectronic sensor is designed as a frequency-modulated continuous wave (FMCW) LiDAR sensor. The fundamentals of FMCW LiDAR technology are described, for example, in the scientific publication "Linear FMCW Laser Radar for Precision Range and Vector Velocity Measurements" (Pierrottet, D., Amzajerdian, F., Petway, L., Barnes, B., Lockard, G., & Rubio, M. (2008). Linear FMCW Laser Radar for Precision Range and Vector Velocity Measurements. MRS Proceedings, 1076, 1076-K04-06. doi:10.1557 / PROC-1076-K04-06) or the doctoral thesis "Realization of Integrated Coherent LiDAR" (T. Kim, University of California, Berkeley, 2019. https: / / escholarship.org / uc / item / 1d67v62p).

[0023] Unlike LiDAR sensors or laser rangefinders based on time-of-flight measurement of laser pulses, an FMCW LiDAR sensor emits continuous light beams into a monitoring area, rather than pulsed ones. During a measurement—that is, a time-discrete sampling of a measurement point within the monitoring area—these beams exhibit a predefined frequency modulation, meaning a temporal change in the wavelength of the transmitted light. The measurement frequency of an entire frame, which can consist of 100,000 measurement points or more, is typically in the range of 10 to 30 Hz. The frequency modulation can, for example, be implemented as a periodic step-up and step-down modulation.Transmitted light reflected from measurement points within the monitoring area exhibits a time delay compared to the transmitted light, corresponding to the light travel time. This time delay depends on the distance of the measurement point from the sensor and is accompanied by a frequency shift due to frequency modulation. In the FMCW LiDAR sensor, the transmitted and reflected light are coherently superimposed, and the distance of the measurement point from the sensor can be determined from the superimposed signal. Compared to pulsed or amplitude-modulated incoherent LiDAR measurement principles, the coherent superimposition measurement principle offers, among other advantages, increased immunity to ambient light from sources such as other optical sensors / sensor systems or sunlight.

[0024] If a measurement point moves towards or away from the sensor with a radial velocity, the reflected transmitted light also exhibits a Doppler shift. An FMCW LiDAR sensor can determine this change in the transmitted light frequency and, from this, calculate the distance and radial velocity of a measurement point in a single measurement, i.e., a single scan of the measurement point. In contrast, a LiDAR sensor based on the time-of-flight measurement of laser pulses requires at least two measurements, i.e., two time-spaced scans of the same measurement point, to determine the radial velocity.

[0025] When scanning a three-dimensional monitoring area in a discrete time and space, an FMCW LiDAR sensor can acquire or generate the following measurement data: M j , k , l = r j , k , l v j , k , l r I j , k , l .

[0026] Here, they refer to rj,k,l the radial distance, vr< j,k,l the radial velocity and I j,k,lthe intensity of each spatially discrete measurement point j,k with two-dimensional, by azimuth angle φ and polar angle θ specified position (φ j, θ k ) for each time-discrete sample I For ease of reading, the following index n for a one-time time-discrete sampling of a space-discrete, two-dimensional measurement point ( φ j , θ k ) in the three-dimensional monitoring area. The measurement data generated by the FMCW LiDAR sensor thus includes, in addition to the position and intensity information of the measurement points, their radial velocities.

[0027] In the case of single-mode emitting FMCW LiDAR sensors, very small laser beams with diameters in the millimeter range can be generated to scan the measurement area. Due to the high measurement information density of the FMCW LiDAR sensor, less complex and more robust algorithms can be used to determine the positioning information of an aircraft relative to its parking position.

[0028] The control and evaluation unit can be configured to segment the measurement points using their radial velocities. By using spatially resolved radial velocity as an additional parameter to the usual position and intensity information of the measurement points, improved segmentation of the measurement data is possible.

[0029] The control and evaluation unit is designed to determine a motion pattern of the first segment using spatially resolved radial velocities from measurement points assigned to at least one first segment. A motion pattern is understood to be a characteristic intrinsic motion of the segment, in the form of a rotation or a relative motion of the segment to the aircraft itself or to another segment, which results from a radial velocity profile of the measurement points assigned to the segment. Such a characteristic intrinsic motion or characteristic radial velocity profile can, for example, be stored as a predetermined motion pattern in the control and evaluation unit, acquired during a learning process, or determined during operation of the device using machine learning or artificial intelligence methods.

[0030] The use of spatially resolved radial velocities to determine a motion pattern of the measurement points assigned to a first segment has the advantage that the motion pattern of the segment can be determined quickly and reliably.

[0031] Determining the movement pattern of a segment, in particular determining the relative movement between different segments or of segments to the aircraft itself, can be used, for example, to identify the rotating engine blades and thus the aircraft's engine.

[0032] The movement patterns of an aircraft's wheels can, for example, improve the detection of the aircraft's landing gear, as these patterns differ from those of the fuselage. Since the nose landing gear typically has different tire sizes than the main landing gear, the movement patterns of the corresponding wheels also differ, thus enabling improved differentiation and localization of the main and nose landing gear.

[0033] In one embodiment, the control and evaluation unit can be configured to filter the measurement data using the radial velocities of the measurement points. This allows the computational effort to be reduced through data reduction even before the measurement points are segmented. Filtering can be achieved, for example, by discarding measurement points with a radial velocity less than, greater than, or equal to a predefined threshold value and excluding them from further evaluation. This allows, for instance, measurement points belonging to a moving aircraft to be separated from those belonging to a static background, thereby reducing the amount of data for subsequent processing steps.

[0034] In one embodiment, the control and evaluation unit can be configured to determine the aircraft's speed using the radial velocity of the measurement points and to compare this speed with a predefined speed limit. The aircraft's positioning information can then include the aircraft's speed and / or information about an exceedance of the predefined speed limit. The aircraft's speed, or a 3D velocity vector, can be calculated, for example, using the speed estimation method described in "Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects (Guo et al., 2022)."

[0035] The FMCW LiDAR sensor can additionally be configured to detect polarization-dependent intensities of the transmitted light reflected or remitted by the measurement points. For this purpose, the FMCW LiDAR sensor has an output coupling unit designed to extract at least a portion of the transmitted light reflected or remitted by the measurement points in the monitoring area, hereinafter also referred to as received light, and direct it to a polarization analyzer. The polarization analyzer is configured to measure the polarization-dependent intensities of the received light, for example, by polarization-dependent splitting of the received light using a polarizing beam splitter cube or a metasurface, and measuring the intensities of the split received light using suitable detectors.

[0036] When scanning a three-dimensional monitoring area in a time- and space-discrete manner, an FMCW LiDAR sensor configured in this way can thus acquire the following measurement data: M j , k , l = r j , k , l v j , k , l r I ⊥ j , k , l I ∥ j , k , l .

[0037] Here, they refer to rj,k,l the radial distance, vr< j,k,l the radial velocity as well as I ⊥ j,k,l and I ∥ j,k,l the polarization-dependent intensities of each spatially discrete measurement point j, k with two-dimensional, by azimuth angle φ and polar angle θ specified position ( φ j, θ k ) for each time-discrete sample I. For ease of reading, the following index n for a one-time time-discrete sampling of a space-discrete, two-dimensional measurement point ( φ j, θ k ) used in three-dimensional monitoring.

[0038] To evaluate the polarization-dependent intensities additionally recorded by the FMCW LiDAR sensor, the control and evaluation unit can be designed to segment the measurement points using the spatially resolved radial velocity of the measurement points and the polarization-dependent intensities of the transmitted light reflected or remitted by the measurement points, and to assign them to segments of the aircraft.

[0039] By using the spatially resolved radial velocity and the polarization-dependent intensities of the transmitted light reflected or remitted from the measurement points as additional parameters, further improved segmentation of the measurement data is possible. For example, evaluating the polarization-dependent intensities can improve the detection of cockpit windows of an aircraft, since transmitted light rays reflected from the cockpit window panes differ in their polarization-dependent intensities from those reflected from the fuselage of the aircraft.

[0040] The FMCW LiDAR sensor can be mounted on a passenger boarding bridge and scan a predefined monitoring area, for example, the apron in front of the boarding bridge. Preferably, at least one further FMCW LiDAR sensor can be provided to scan another monitoring area, with the monitoring areas potentially overlapping. This avoids shadowing or blind spots where object detection is not possible. If two or more FMCW LiDAR sensors are arranged such that orthogonal measurement beams can be generated, the velocity vector of an object scanned by these beams in the plane spanned by the orthogonal beams can be determined by combining these beam pairs.

[0041] The sensor can be designed as a safety sensor, for example, in accordance with the EN13849 standard for machine safety and the IEC61496 or EN61496 standard for non-contact protective devices (NCPDs). In this case, the sensor operates with exceptional reliability and meets high safety requirements, and may feature redundant, diverse electronics, redundant functional monitoring, or special monitoring for contamination of optical components. The control and evaluation unit can then be configured, in particular, to aggregate measurement points for objects that are not part of the aircraft, such as people or vehicles on the apron, extract characteristics of these objects, and, for example, trigger a safety-related action upon detection of an unauthorized object on the apron, such as emitting a visual and / or audible warning signal.This means the device can be used for apron monitoring in addition to positioning the aircraft.

[0042] The control and evaluation unit can include at least one digital processing module and be integrated into or connected to the sensor, for example in the form of a higher-level control system that transmits the aircraft's positioning information to the aircraft itself. At least parts of the functionality can also be implemented in a remote system or a cloud.

[0043] The method according to the invention can be further developed in a similar manner and exhibits similar advantages. Such advantageous features are described by way of example, but not exhaustively, in the dependent claims following the independent claims.

[0044] The invention is further explained below with regard to additional features and advantages by way of example embodiments and with reference to the accompanying drawing. The illustrations in the drawing show: Fig. 1 an example of a radial velocity measurement with an FMCW LiDAR sensor; Fig. 2 a schematic representation of a device according to the invention for detecting an aircraft in a side view; Fig. 3 a schematic representation of a device according to the invention for detecting an aircraft in a frontal view; Fig. 4 a flowchart for an exemplary processing of measurement data from an FMCW LiDAR sensor according to the invention.

[0045] In Figure 1The concept of radial velocity measurement with an FMCW LiDAR sensor 12 is shown using a three-dimensional example. If an object 38 moves with a velocity v 0 along a direction of motion 40 relative to the FMCW LiDAR sensor 12, the FMCW LiDAR sensor 12 can measure the radial distance. r and the intensity I of a measurement point 20 sampled once in a time-discrete manner with a transmitted light beam 14 at an azimuth angle φ and a polar angle θ, the radial velocity v r< The position of measuring point 20 of object 38 in the direction of the FMCW LiDAR sensor 12 is determined. This information is directly available with a single measurement, i.e., a discrete-time scan of measuring point 20. Thus, unlike measurement methods that only provide spatially resolved radial distances, i.e., three-dimensional positions, this method eliminates the need for a second measurement and, in particular, the need to first determine the measurement points in the measurement data of the second measurement that correspond to the measurement points of the first measurement.

[0046] In the case of a static FMCW LiDAR sensor, each measurement point with a radial velocity of zero is generally assigned to a static object, unless the object is moving tangentially to the sensor's measurement beam. Due to the finite object size and the high spatial resolution of the FMCW LiDAR sensor, practically every moving object will have at least one measurement point 20 with a non-zero radial velocity. vr< n The FMCW LiDAR sensor 12 exhibits this capability. Therefore, a single measurement from the FMCW LiDAR sensor 12 can distinguish between static and moving objects. For example, when detecting a moving aircraft, static objects can be discarded. This data reduction reduces the computational effort required for further analysis of the measurement data.

[0047] Figure 2Figure 10 shows a schematic representation of a device 10 according to the invention for positioning an aircraft 22, shown in side view, on an apron 24 of an airport. An FMCW LiDAR sensor 12 emits light beams 14.1, ..., 14.n into a three-dimensional monitoring area 16 on the apron 24 and generates measurement data. M n 18 from measurement points 20.1, ..., 20.n in the monitoring area 16 back to the FMCW LiDAR sensor 12, reflected or remitted transmitted light. For illustration purposes, a limited number of exemplary transmitted light beams 14.1, ..., 14.n and measurement points 20.1, ..., 20.n are shown; the actual number results from the size of the monitoring area 16 and the spatial resolution of the scanning. The measurement points 20.1, ..., 20.n can represent the aircraft 22 located in the monitoring area 16, but also persons 26, vehicles (not shown), or the apron 24 itself.

[0048] The measurement data received by a control and evaluation unit 32 M n 18 of the FMCW LiDAR sensor 12 include, for each discrete-time scan, in addition to the radial distances r n and the intensities In , i.e., the amount of transmitted or reflected light, in particular the radial velocities vr< n the measuring points 20.1, ..., 20.n, where with radial velocity vr< n the velocity component of a measurement point 20.1, ..., 20.n is designated with which the measurement point 20.1, ..., 20.n moves towards or away from the FMCW LiDAR sensor 12.

[0049] The control and evaluation unit 32 comprises at least one digital processing component, for example, at least one microprocessor, at least one FPGA (Field Programmable Gate Array), at least one DSP (Digital Signal Processor), at least one ASIC (Application-Specific Integrated Circuit), at least one VPU (Video Processing Unit), or at least one neural processor. Furthermore, the control and evaluation unit 32 can be located at least partially external to the FMCW LiDAR sensor 12, for example, in a higher-level control system, a connected network, an edge device, or a cloud.

[0050] The measurement data M n18 are evaluated by the control and evaluation unit 32, the control and evaluation unit 32 being configured to segment the measuring points 20.1, ..., 20.n, to combine them at least partially into segments of the aircraft 22, such as the fuselage 22.2, the wheels 22.2 of the main landing gear, the engines 22.3, the cockpit window 22.4 or the aircraft nose 22.5, to extract features of the segments 22.1, ..., 22.n, to assign the segments 22.1, ..., 22.n to an aircraft type from a multitude of aircraft types based on the extracted features, and to output positioning information for the aircraft 22 via an interface 34 of the control and evaluation unit 32 based on the assigned aircraft type. For example, an output unit 36 ​​can be connected to interface 34, which displays positioning information to a pilot, for example in the form of a distance to a type-specific parking position of the aircraft 22.

[0051] The control and evaluation unit 32 can, for example, be used with the radial velocities vr< n Determine the movement patterns of the object segments 22.1, ..., 22.5 at measuring points 20.1, ..., 20.n, for example a rotation 26 of the wheels 22.2 of the main landing gear, and use the recorded movement patterns to extract features of the segments 22.1, ..., 22.n.

[0052] The control and evaluation unit 32 can continue to be used with the radial velocities vr< n Determine the velocity v 0 of the measuring points 20.1, ..., 20.n along a direction of motion 27 of the aircraft 22, for example using a method as described in the scientific publication "Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects (Guo et al., 2022)".

[0053] Figure 3 shows a schematic representation of the device 10 according to the invention. Figure 2, where aircraft 22 is shown in a frontal view. Identical parts are labelled with identical reference numerals. The FMCW lidar sensor 12 is mounted on a passenger boarding bridge 28.

[0054] The control and evaluation unit (not shown here) can, for example, be used using the radial velocities. vr< n From measuring points that detect the turbine blades 30 of engine 22.3, a movement pattern of the turbine blades 30 can be determined, for example, a rotation 32 of the turbine blades 30. Since the turbine blades exhibit a very specific movement pattern due to their rotation, which differs significantly from the movement patterns of other segments of the aircraft 22, the position of the turbine blades 30 or of engine 22.3 can be determined particularly reliably.

[0055] Figure 4Figure 42 shows an exemplary processing according to the invention of the measurement data acquired by the FMCW LiDAR sensor 12 by the control and evaluation unit 32. After receiving the measurement data 44, the measurement points 20.1, ..., 20.n are segmented in a segmentation step 46 and combined into segments 22.1, ..., 22.5, of the aircraft 22, wherein, in addition to the spatial coordinates and intensities of the measurement points usually used for segmentation 46, the spatially resolved radial velocities are particularly important. vr< n The measuring points 20.1, ..., 20.n can be taken into account. Segments of the aircraft can be, for example, the fuselage 22.2, the wheels 22.2 of the main landing gear, the engines 22.3, the cockpit window 22.4 or the aircraft nose 22.5.

[0056] The segmentation 46 can be carried out, for example, according to the above-mentioned methods of digital image processing or machine vision or "range segmentation".

[0057] By using radial velocity vr< n in addition to the radial spacing r n and the intensity In The segmentation of measurement points 20.1, ..., 20.n can be carried out more efficiently and accurately using the methods listed above. For example, measurement points 20.1, ..., 20.n, with radial velocities vr< nData smaller than, greater than, or equal to a predefined threshold are discarded and not subjected to further evaluation. If an object such as the aircraft 22 and / or object segment such as the aircraft nose 22.5 is scanned by several spatially discrete measurement points and the associated radial velocities are distinguishable, static and dynamic objects and / or object segments can be differentiated, and thus stationary objects such as the apron 24 can be discarded before or during the segmentation 46 of the measurement points 20.1, ..., 20.n, and the computational effort can be reduced through data reduction.

[0058] In the next step, feature extraction 48 is performed from the segments 22.1, ..., 22.5 defined during the segmentation 46. Typical features that can be extracted from segments 22.1, ..., 22.5 during the processing of the measurement data are, for example, width, number of measurement points or length of the perimeter of the segments, or other features as described, for example, in the scientific publication "A Layered Approach to People Detection in 3D Range Data" (Spinello et al., Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010).

[0059] Following feature extraction 48, segments 22.1, ..., 22.5 are assigned or classified 50 to an aircraft type from a large number of aircraft types using known classification methods such as Bayesian classifiers, support vector machines, or artificial neural networks. During the assignment process, the feature space is searched for groups of features that define a segment 22.1, ..., 22.5.

[0060] Based on the result of classification 50, an output 52 of positioning information for aircraft 22 can be generated based on the identified aircraft type. This positioning information can include distance and / or direction data to an aircraft-type-specific parking position for aircraft 22 and can be displayed on a display unit for the aircraft crew.

Claims

1. Device (10) for positioning an aircraft (22) in a monitoring area (16) of an apron (24) of an airport with at least one optoelectronic sensor (12) for emitting beams of transmitting light (14, ..., 14.n) into the monitoring area (16), for scanning a multitude of measurement points (20.1, ..., 20n) and for generating measurement data (18) from transmitting light emitted or reflected from the measurement points (20.1, ..., 20n), - a control and evaluation unit (32) for the evaluation of the measurement data (18), wherein the control and evaluation unit (32) is configured to segment the measurement points (20.1, ..., 20n), to combine the measurement points (20.1, ..., 20n) at least partially into segments (22.1, ..., 22.5) of the aircraft (22), to extract characteristics of the segments (22.1, ..., 22.5), to assign the segments (22.1, ..., 22.5) to an aircraft type selected from a multitude of aircraft types on the basis of the extracted characteristics and to output a positioning information for the aircraft (22) on the basis of the assigned aircraft type. characterized in that the at least one optoelectronic sensor (12) is an FMCW LIDAR sensor, the measurement data (18) include radial velocities (vrn) of the measurement points (20.1, ..., 20n), and the control and evaluation unit (32) is designed to use radial velocities (vrn) of measurement points (20.1, ..., 20.5) assigned to at least one first segment (22.1, ..., 22.5) to determine a rotation or relative motion of at least one first segment (22.1, ..., 22.5) to the aircraft (22) itself or to a further segment (22.1, ..., 22.5).

2. The device according to claim 1, characterized in that the control and evaluation unit (32) is configured to segment the measurement points (20.1, ..., 20n) using the radial velocities (vrn) of the measurement points (20.1, ..., 20n).

3. The device according to any one of the preceding claims, characterized in that the control and evaluation unit (32) is configured to assign the measurement points (20.1, ..., 20n) to segments (22.1, ..., 22.5n) of the aircraft (22), at least partially using the radial velocities (vrn) of the measurement points (20.1, ..., 20n).

4. The device according to any one of the preceding claims, characterized in that the control and evaluation unit (32) is configured to extract features of the segments (22.1, ..., 22.5) using the radial velocities (vrn) of the measurement points (20.1, ..., 20.5). assigned to the segments (22.1, ..., 22.5).

5. The device according to any of the preceding claims, wherein the control and evaluation unit (32) is designed to filter the measurement points (20.1, ..., 20n) using the radial velocity (vrn) of the measurement points (20.1, ..., 20n).

6. The device according to any one of the preceding claims, wherein the control and evaluation unit (32) is configured to determine a velocity (v0) along a direction of motion (27) of the aircraft (22) using the radial velocity (vrn) of the measurement points (20.1, ..., 20n).

7. The device according to any one of the preceding claims, characterized in that the FMCW LiDAR sensor is configured to detect polarization-dependent intensities (I⊥n, I∥n) of the transmitting light remitted or reflected from the measurement points (20.1, ..., 20n) and the measurement data (18) comprise the polarization-dependent intensities (I⊥n, I∥n).

8. The device according to claim 7, characterized in that the control and evaluation unit (32) is configured to segment the measurement points (20.1, ..., 20n) using the polarization-dependent intensities (I⊥n, I∥n) and to at least partially combine the measurement points (20.1, ..., 20n) into segments (22.1, ..., 22.5) of the aircraft (22).

9. The device according to claim 7, characterized in that the control and evaluation unit (32) is configured to filter the measurement points (20.1, ..., 20n) using the polarization-dependent intensities (I⊥n, I∥n).

10. The device according to any one of the preceding claims, wherein the device comprises at least one further FMCW LiDAR sensor with a further monitoring area and the monitoring area (16) overlaps at least partially with the further monitoring area.

11. Method for detecting an aircraft (22) in a monitoring area (16) of an apron (24) of an airport with the steps of: - emitting transmitting light beams (14, 14.1, ..., 14.n) into the monitoring area (16) with at least one FMCW LiDAR sensor (12), - scanning a multitude of measurement points (20.1, ..., 20n) in the monitoring area (16), - generating measurement data (18) from transmitting light emitted or reflected from the measurement points (20.1, ..., 20n), wherein the measurement data (18) include radial velocities (vrn) of the measurement points (20.1, ..., 20n), - segmenting (46) the measurement points (20.1, ..., 20n) and at least partially combining the measurement points (20.1, ..., 20.5) into segments (22.1, ..., 22.5) of the aircraft (22); - extracting (48) features of the segments (22.1, ..., 22.5); - assigning (50) the segments (22.1, ..., 22.5) to an aircraft type selected from a multitude of aircraft types based on the extracted features; - outputting a positioning information for the aircraft (22) based on the assigned aircraft type, characterized by the further step - determining a rotation or relative motion of at least one first segment (22.1, ..., 22.5) to the aircraft (22) itself or to a further segment (22.1, ..., 22.5) using the radial velocities (vrn) of the measurement points (20.1, ..., 20.5) assigned to the first segment (22.1, ..., 22.5).

12. The method of claim 11, wherein the segmenting (48) of the measurement points (20.1, ..., 20n) and the at least partial combining of the measurement points (20.1, ..., 20.5) to the object segments (22.1, ..., 22.5) is carried out using the radial velocities (vrn) of the measurement points (20.1, ..., 20n).

13. A method according to any one of claims 11 or 12 having the further step of: - filtering the measurement points (20.1, ..., 20n) using the radial velocities (vrn) of the measurement points (20.1, ..., 20n).