DE202021100171U1 - Device for securing a surveillance area - Google Patents

Abstract

Device (10) for securing a surveillance area (16).
- at least one FMCW LiDAR sensor (12) for emitting transmitted light beams (14, 14.1, 14.2, 14.3, 14.n) into the monitoring area (16), for scanning a large number of measuring points (20, 20.1, 20.2, 20.3, 20.n) and for generating measurement data (18) from the transmission light remitted or reflected by the measurement points (20, 20.1, 20.2, 20.3, 20.n), and
- A control and evaluation unit (32) for evaluating the measurement data (18) and for generating a safety-related signal based on the evaluation, characterized in that the measurement data (18) radial speeds (v ^r _n ) of the measuring points (20, 20.1, 20.2 , 20.3, 20.n), and the control and evaluation unit (32) is designed to measure the measuring points (20, 20.1, 20.2, 20.3, 20.n) using the radial speeds (v ^r _n ) of the measuring points (20 , 20.1, 20.2, 20.3, 20.n) and to combine them into objects (22, 24, 26, 28, 30) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3).

Description

The invention relates to a system for securing a surveillance area according to the preamble of claim 1.

The primary goal of safety technology is to protect people from sources of danger, such as those presented by machines in an industrial environment. The machine is monitored with the help of sensors, and accordingly, if there is a situation where a person threatens to come dangerously close to the machine, an appropriate safety measure is taken.

Optoelectronic sensors such as laser scanners or 3D cameras are often used for safety monitoring. Sensors used in safety technology must work particularly reliably and therefore meet high safety requirements, for example the EN13849 standard for machine safety and the IEC61496 or EN61496 device standard for electro-sensitive protective equipment (ESPE). A number of measures must be taken to meet these safety standards, such as reliable electronic evaluation using redundant, diverse electronics, function monitoring or special monitoring of the contamination of optical components.

In the DE 10 2007 007 576 A1 a machine is safeguarded by a large number of laser scanners recording a three-dimensional image of its working area and comparing this actual state with a target state. The laser scanners are positioned on tripods at different heights at the edge of the work area. 3D cameras can also be used instead of laser scanners.

From the DE 198 43 602 A1 a method and a device for detecting the movements of process units during a production process in a predetermined evaluation area are known. At least two cameras fixed in the evaluation area are used. Location coordinates of each process unit are continuously recorded and a translation vector describing the movement of the respective process unit is determined for each location coordinate.

the U.S. 9,804,576 B2 discloses a detection-based, industrial automation control that is set up to detect human movements, to derive them for the future and to compare them with planned automation commands in order to derive further safety-related actions (alarms or changed control commands), if necessary, using 3D cameras to detect human movements.

the DE 10 2006 048 163 B4 describes camera-based monitoring of moving machines and/or moving machine elements to prevent collisions, image data of the machine and/or moving machine elements being recorded with the aid of an image acquisition system. The image acquisition system can in particular be a multi-ocular camera system; LiDAR, RADAR or ultrasonic sensors are also mentioned as possible image acquisition systems.

However, the image acquisition systems usually used in the prior art for object and/or person detection, in particular laser scanners and camera systems, have disadvantages which will be discussed in more detail below.

Laser scanners or LiDAR (Light Detection And Ranging) sensors are mostly based on a direct time-of-flight measurement. A light pulse is emitted by the sensor, reflected on an object and then detected again by the sensor. The transit time of the light pulse is determined by the sensor and the distance between the sensor and the object is estimated via the speed of light in the propagation medium (usually air). Since the phase of the electromagnetic wave is not taken into account, this is referred to as an incoherent measuring principle. In an incoherent measurement, there is a need to build up pulses from many photons in order to receive the reflected pulse with a sufficient signal-to-noise ratio. In an industrial environment, the number of photons within a pulse is usually limited by eye protection. As a result, considerations arise between maximum range, minimum remission of the object, integration time and the requirements for the signal-to-noise ratio of the sensor system. In addition, incoherent radiation at the same wavelength (ambient light) has a direct effect on the dynamic range of the light receiver. Examples of incoherent radiation with the same wavelength are the sun, similar sensor systems, or the identical sensor system via multipath propagation, i.e. unwanted reflections.

Camera systems known from the prior art are based on measurement principles such as stereoscopy or indirect time-of-flight measurement. With indirect time-of-flight measurement, the phase difference between an AMCW (Amplitude Modulated Continuous Wave) transmission signal and its time-delayed copy is determined after reflection with an object. The phases difference corresponds to the transit time of light and can be converted into a distance value via the speed of light in the propagation medium. Both stereoscopy and indirect time-of-flight measurement are also incoherent measurement methods with the disadvantages mentioned above.

Millimeter wavelength radar sensors are based on a frequency-modulated continuous wave (FMCW) measuring principle and can also determine the radial velocities of a detected object using the Doppler effect. The biggest disadvantage of millimeter wavelength radar sensors compared to optical technologies is the significantly longer wavelength and the resulting lower spatial resolution. In addition, regulatory requirements limit the radial resolution by limiting the bandwidth and in a MIMO (Multiple Input Multiple Output) - radar system the number of available virtual antennas (product of the number of transmitting and receiving antennas) the angular resolution. Compared to optical technologies, geometric physical features can therefore hardly be used for safety-related object and/or person detection.

It is therefore the object of the invention to improve a device for protecting a surveillance area with an optoelectronic sensor.

This object is achieved by a device for securing a surveillance area according to claim 1.

The device according to the invention for securing a monitoring area has at least one optoelectronic sensor, which can be arranged on a machine or in a stationary manner.

According to the invention, the optoelectronic sensor is designed as a frequency-modulated continuous wave (FMCW) LiDAR sensor. The basics 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).

In contrast to a LiDAR sensor based on time-of-flight measurement of laser pulses, an FMCW LiDAR sensor does not emit pulsed, but continuous transmitted light beams into the monitoring area, which during a measurement, i.e. a time-discrete scanning of a measuring point in the monitoring area, have a specified frequency modulation, i.e a change in the wavelength of the transmitted light over time. The measurement frequency is typically in the range from 10 to 30 Hz. The frequency modulation can be designed as periodic up and down modulation, for example. In comparison to the transmitted light emitted, the transmitted light reflected from measuring points in the monitoring area has a time delay corresponding to the light propagation time, which depends on the distance of the measuring point from the sensor and is associated with a frequency shift due to the frequency modulation. In the FMCW LiDAR sensor, emitted and reflected transmitted light are superimposed coherently, and the distance of the measuring point from the sensor can be determined from the superimposed signal. Compared to pulsed or amplitude-modulated incoherent LiDAR measuring principles, the measuring principle of coherent superimposition has the advantage, among other things, of increased immunity to extraneous light from, for example, other optical sensors/sensor systems or the sun. Compared to radar sensors with wavelengths in the millimeter range, the spatial resolution is improved, making it possible to measure a person's geometric properties.

If a measurement point moves towards or away from the sensor at a radial speed, the reflected transmitted light also shows a Doppler shift.

An FMCW LiDAR sensor can determine this change in the transmitted light frequency and use it to determine the distance and radial velocity of a measuring point in a single measurement, i.e. a single scan of a measuring point, while a LiDAR sensor based on the time-of-flight measurement of laser pulses is used to determine the Radial velocity at least two measurements, ie two time-spaced samples of the same measurement point, are necessary.

When scanning a three-dimensional surveillance area discretely in terms of time and space, an FMCW LiDAR sensor can record the following measurement data: $$M_{j,k,l}=\left(\begin{array}{c}{\mathrm{right}}_{j,k,l}\\ v_{j,k,l}^{\mathrm{right}}\\ I_{j,k,l}\end{array}\right).$$

Here r _j,k,l denote the radial distance, v ^r _j,k,l the radial velocity and l _j,k,l the intensity of each spatially discrete measuring point j, k with a two-dimensional position (φ _j , θ _k ) for each discrete-time sample l. For easy readability, the index n is used below for a one-off time-discrete sampling of a space-discrete, two-dimensional measurement point (φ _j , θ _k ) in the three-dimensional surveillance area.

To evaluate the measurement data recorded by the FMCW LiDAR sensor and to trigger a safety-related action based on the evaluation, the device according to the invention has a control and evaluation unit that is designed to segment and assign the measurement points using the spatially resolved radial velocity of the measurement points To summarize objects and / or object segments. Object segments are to be understood as meaning individually movable parts of an object consisting of several parts, for example the limbs of a human body or the components of a robot arm.

The invention has the advantage that improved segmentation of the measurement data is possible through the use of the spatially resolved radial velocity as an additional parameter. This also applies in particular to known segmentation methods of digital image processing or machine vision.

The control and evaluation unit can also be designed to determine radial velocities of the objects and/or object segments and to extract features of the objects and/or object segments that are based on the radial velocities of the object segments. The extracted features can be, for example, statistical dimensions such as mean value or standard deviation, higher moments, or histograms of the radial speeds of the objects and/or object segments, which can be characteristic of an object and/or object segment movement.

Advantageously, the control and evaluation unit can be designed to use the features based on the radial speeds of the objects and/or object segments to classify the objects and/or object segments. These additional features enable an improved classification of the objects and/or the object segments.

In one embodiment, the control and evaluation unit can be designed to filter the measurement data using the radial speeds of the measurement points. Thus, the computing effort can be reduced by data reduction even before a segmentation of the measurement points. Filtering can be carried out, for example, by discarding measurement points with a radial speed that is less than, greater than, or equal to a predetermined threshold value and not feeding them to any further evaluation. For example, in the case of an anti-collision function, objects and/or object segments moving with the sensor (v ^r =0) or moving away from the sensor (v ^r >0) can be discarded.

The FMCW LiDAR sensor can be stationary and can scan a predetermined surveillance area. At least one further FMCW LiDAR sensor can preferably be provided, which scans a further monitoring area, in which case the monitoring areas can overlap. As a result, shadows or blind spots in which no object detection is possible can be avoided. If two FMCW-LiDAR sensors are arranged orthogonally to one another, one FMCW-LiDAR sensor can determine the tangential component of the velocity vector of an object of the other FMCW-LiDAR sensor.

The FMCW LiDAR sensor can be arranged on a machine, in particular on an automated guided vehicle (AGV) or on a robot.

The robot can be in motion as a whole (mobile robot) or perform movements using different axes and joints. The sensor can then follow movements of the machine and scan a variable monitoring area.

The sensor is preferably safe in the sense of the standards mentioned in the introduction or comparable ones. The control and evaluation unit can be integrated into the sensor or connected to it, for example in the form of a safety controller or a higher-level controller that also communicates with the machine controller. At least parts of the functionality can also be implemented in a remote system or a cloud.

The sensor can preferably be attached to or near a hazardous machine part, such as a tool tip. For example, if it is a robot with many axes, their interaction is not relevant for the sensor, since the sensor simply follows the resulting movement at the location of the danger.

In a development of the invention, several optoelectronic sensors can be attached to the machine in order to determine the movement of moving parts of the machine. This means that even complex machines can be monitored for which a selective determination of the movement is not sufficient. An example is a robot with multiple robotic arms and possibly joints. At least one stationary optoelectronic sensor, i.e. one that is not moved with the machine, can also monitor the machine.

• 1 eine schematische Darstellung einer erfindungsgemäßen Vorrichtung mit einem stationär angeordneten FMCW-LiDAR-Sensor;
• 2 ein Beispiel für eine Radialgeschwindigkeitsmessung mit einem FMCW-LiDAR-Sensor;
• 3 ein Ablaufdiagram für eine beispielhafte erfindungsgemäße Verarbeitung von Messdaten eines FMCW-LiDAR-Sensors;
• 4 ein beispielhaftes Ablaufdiagramm zur Überwachung einer Bewegung eines Roboters unter Verwendung einer erfindungsgemäßen Vorrichtung.
• 5 ein beispielhaftes Ablaufdiagramm zur Kollisionsvermeidung unter Verwendung einer erfindungsgemäßen Vorrichtung.
• 6 ein beispielhaftes Ablaufdiagramm zur Personenidentifikation unter Verwendung einer erfindungsgemäßen Vorrichtung.

The invention is explained in more detail below, also with regard to further features and advantages, by way of example on the basis of embodiments and with reference to the attached drawing. The illustrations of the drawing show in:

• 1 a schematic representation of a device according to the invention with a stationary arranged FMCW-LiDAR sensor;
• 2 an example of a radial velocity measurement with an FMCW LiDAR sensor;
• 3 a flowchart for an exemplary inventive processing of measurement data from an FMCW LiDAR sensor;
• 4 an exemplary flow chart for monitoring a movement of a robot using a device according to the invention.
• 5 an exemplary flowchart for collision avoidance using a device according to the invention.
• 6 an exemplary flowchart for personal identification using a device according to the invention.

1 shows a schematic representation of a device 10 according to the invention with a stationary FMCW LiDAR sensor 12. The FMCW LiDAR sensor 12 emits transmitted light beams 14.1, ..., 14.n in a three-dimensional monitoring area 16 and generates measurement data M _n 18 from transmitted light reflected or remitted from measuring points 20.1, ..., 20.n in the monitoring area 16 back to the FMCW LiDAR sensor 12. A limited number of exemplary transmitted light beams 14.1, ..., 14.n and measuring points 20.1, ..., 20.n are shown for the purpose of illustration; the actual number results from the size of the monitoring area 16 and the spatial resolution of the scanning. The measuring points 20.1, ..., 20.n can be persons 22 located in the monitored area 16, machines, for example robots 24 or driverless vehicles (automated guided vehicles, AGV) 26, static objects such as boxes 28, or also limits of the monitored area such as floors 30 or represent walls.

The measurement data M _n 18 of the FMCW LiDAR sensor 12 received by the control and evaluation unit 32 include for each time-discrete sampling, in addition to the radial distances r _n and the intensities I _n , i.e. the remitted or reflected transmitted light quantity, in particular the radial velocities v ^r _n der Measuring points 20.1, ..., 20.n, the radial speed v ^r _n denoting the speed component of a measuring point 20.1, ..., 20.n, with which the measuring point 20.1, ..., 20.n is on the FMCW -LiDAR sensor 12 moved towards or away from FMCW LiDAR sensor 12.

The measurement data M _n 18 are evaluated by a control and evaluation unit 32, the control and evaluation unit 32 being set up to generate a safety-related signal for triggering a safety-related action based on the evaluation. The safety-related action can, for example, involve activating a warning light 34 or stopping the robot 24 . In the exemplary embodiment, the control and evaluation unit 32 is connected directly to the warning light 34 and the robot 24, ie triggers the safety-related action itself. Alternatively, the control and evaluation unit 32 can forward a safety-related signal to a higher-level safety controller (not shown) via an interface 36, or the control and evaluation unit 32 can itself be part of a safety controller.

In 2 shows the concept of radial velocity measurement using a three-dimensional example. If an object 38 moves along a direction of movement 40 relative to the FMCW-LiDAR sensor 12, the FMCW-LiDAR sensor 12 can, in addition to the radial distance r and the intensity I, sampled once with a transmitted light beam 14 at an azimuth angle φ and a polar angle θ in a time-discrete manner Measuring point 20 determine the radial speed v ^r of measuring point 20 of object 38 in the direction of FMCW LiDAR sensor 12 . This information is available directly with a measurement, that is to say a time-discrete scanning of the measuring point 20 . In contrast to measurement methods that only provide spatially resolved radial distances, i.e. three-dimensional positions, there is no 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 in order to identify moving objects correspond to.

Each measurement point with a radial velocity of zero is usually associated with a static object. Due to the finite extent of the object and the high spatial resolution of the FMCW LiDAR sensor, practically every moving object in the surveillance area is 16 have at least one measuring _point ^20.1 , . . . Therefore, a measurement by the FMCW LiDAR sensor 12 can already distinguish between static and moving objects or, in mobile applications, objects that are moving away or approaching. For example, in anti-collision monitoring, measuring points or objects that are moving away can be discarded. A corresponding data reduction reduces the computing effort in the further evaluation of the measurement data M _n 18 .

3 shows in a flowchart 42 an exemplary further processing of the measurement data M _n recorded by the FMCW-LiDAR sensor by the control and evaluation unit of a device according to the invention. After the reception 44 of the measurement data M _n , the measurement points 20.1, . . , 24.3 summarized, with the spatially resolved radial velocities v ^r _n of the measuring points 20.1, ..., 20.n being taken into account in addition to the location coordinates and intensities of the measuring points 20.1, . Object segments can be, for example, individual movable components 24.1, 24.2, 24.3 of a robot 24 or body parts 22.1, 22.2, 22.3 of a person 22.

• - Pixelorientierte Verfahren im Grauwertbild mittels Schwellenwert-Verfahren,
• - Kantenorientierten Verfahren wie dem Sobel- oder Laplace-Operator und einer Gradientensuche,
• - Regionenorientierte Verfahren wie „Region-Growing“, „Region-Splitting“, „Pyramid Linking“ oder „Split and Merge“,
• - Modellbasierte Verfahren wie bspw. die Hough-Transformation, oder
• - Texturorientierte Verfahren.

The segmentation 46 can be carried out using known methods of digital image processing or machine vision, such as, for example

• - Pixel-oriented methods in the gray value image using threshold methods,
• - Edge-oriented methods such as the Sobel or Laplace operator and a gradient search,
• - Region-oriented methods such as "Region-Growing", "Region-Splitting", "Pyramid Linking" or "Split and Merge",
• - Model-based methods such as the Hough transformation, or
• - Texture-oriented methods.

• - „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)

Furthermore, special methods for segmenting three-dimensional data sets are known under the term “range segmentation”. "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)

By using the radial velocity v ^r _n in addition to the radial distance r _n and the intensity I _n of the measurement points 20.1, and done more precisely. For example, ^measurement _points 20.1, . . . In the case of an anti-collision function, for example, measurement points of an object and/or object segment that move with the sensor (v ^r =0) or move away from the sensor (v ^r >0) can be discarded. If an object and/or object segment is scanned by several spatially discrete measuring points and the associated radial velocities can be distinguished, static and dynamic objects and/or object segments can be distinguished and stationary objects and/or object segments such as boxes 28, floors 30 or walls can be identified before or during the segmentation 46 of the measuring points 20.1, ..., 20.n discarded and the computing effort can be reduced by data reduction.

In the next step, a feature extraction 48 of the objects 22, 24, 26, 28, 30 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3 defined during the segmentation 46 takes place. Typical features that can be extracted from objects 22, 24, 26, 28, 30 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3 when processing the measurement data are, for example, the width, number of measurement points or length of the Scope of the objects and/or object segments, or other features, such as those described 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) are described. According to the invention, these features can be expanded to include features based on the radial speeds of objects 22, 24, 26, 28, 30 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3. become First, radial speeds of the objects and/or object segments are determined, for example by applying trigonometric functions to the radial speeds of the measuring points representing the respective object and/or object segment. Statistical measures of the radial velocities of the objects and/or object segments such as mean value, standard deviation, higher moments or histograms, which are characteristic of robot and/or person movements, can then be used as additional object and/or object segment features.

After feature extraction 48, objects 22, 24, 26, 28, 30 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3 are classified 50 using known classification methods such as Bayes classifiers, support vector machines or artificial neural networks . As part of the classification, the feature space is searched for groups of features that define an object. Here, the above statistical measures of the radial speed of individual objects 22, 24, 26, 28, 30 and / or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3 can be used in combination with a priori information to define feature spaces that classify, for example, people 22 and AGVs 26 based on their radial speed and can thus distinguish between them.

After the output 52 , the result of the classification 50 can be further processed by the control and evaluation unit 32 to generate a safety-oriented signal or can be forwarded via the interface 36 to a higher-level controller (not shown).

• • Dewan, Ayush, et al. „Rigid scene flow for 3d lidar scans.“ 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016.

oder

• • Liu, Xingyu, Charles R. Qi, and Leonidas J. Guibas. „Flownet3d: Learning scene flow in 3d point clouds.“ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019.

können die gemessenen Radialgeschwindigkeitswerte als weitere Information, beispielsweise als Randbedingungen, in die Berechnung aufgenommen werden. In einem Vergleichsschritt 60 erfolgt ein Vergleich der Bewegungsanalyse 58 mit a priori Informationen über erwartete Soll-Bewegungen der Segmente 24.1, 24.2., 24.3 des Roboterarms. Bei negativem Ergebnis des Vergleichs 60 (beispielswiese Bewegungsabweichung über ein vorgegebenes Toleranzmaß) wird eine sicherheitsgerichtete Aktion 62, beispielsweise ein Abschalten des Roboters 24 eingeleitet. 4 shows an exemplary flow chart 54 for monitoring a movement of a robot using a device according to the invention. After receipt 44 of the measurement data M _n , the steps of segmentation 46 of the measurement data M _n , feature extraction 48 and classification 50 take place as described above and radial velocities of segments 24.1, 24.2, 24.3. A motion analysis 58 is carried out using algorithms for calculating a “rigid scene flow”, with two sequential samplings M _n,l and M _n,l-1 point by point 6D speed vectors (3D translation+3D rotation) of the previously classified segments 24.1, 24.2, 24.3 can be determined. Compared to the typical determination of the "Rigid Scene Flow" based on the 3D position data such as in

• • Dewan, Ayush, et al. "Rigid scene flow for 3d lidar scans." 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016.

or

• • Liu, Xingyu, Charles R. Qi, and Leonidas J. Guibas. "Flownet3d: Learning scene flow in 3d point clouds." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019.

the measured radial velocity values can be included in the calculation as additional information, for example as boundary conditions. In a comparison step 60, the movement analysis 58 is compared with a priori information about expected desired movements of the segments 24.1, 24.2, 24.3 of the robot arm. If the result of the comparison 60 is negative (for example, a movement deviation exceeds a predetermined tolerance), a safety-related action 62 , for example switching off the robot 24 , is initiated.

5 shows an exemplary flow chart 66 for avoiding a collision of a person 22 and/or their body parts 22.1, 22.2, 22.3 with a machine 24, 26 using a device according to the invention. After receipt 44 of the measurement data _Mn , the steps of segmentation 46 of the measurement data, feature extraction 48 and classification 50 take place as described above in order to identify body parts 22.1, 22.2, 22.3 of the person 22 and/or the person 22 himself. In the following step, a movement prediction 68 of the body parts 22.1, 22.2, 22.3 and/or the person 22 takes place using a Kalman filter. Compared to implementations of a Kalman filter known from radar technology, for example, the higher spatial resolution of an FMCW LiDAR improves its performance. For example, a time-to-collision (TTC) can be determined from the predicted movements as a quantitative measure of the risk of collision 70 and if the comparison result is negative, for example a risk of collision, a safety-related action 72 , for example a warning signal, can be triggered.

6 shows an exemplary flowchart 78 for personal identification using a device according to the invention. After receipt 44 of the measurement data _Mn , the steps of segmentation 46 of the measurement data, feature extraction 48 and classification 50 take place as described above in order to identify body parts 22.1, 22.2, 22.3 of the person 22 and/or the person 22 himself. A movement analysis 58 is carried out using algorithms for calculating the “rigid scene flow”, whereby on the basis of two consecutive Scanning M _n,l and M _n,l-1 6D speed vectors (3D translation+3D rotation) of the body parts 22.1, 22.2, 22.3 and/or the person 22 point by point. Using location coordinates, intensities, radial speeds and 6D speed vectors of the body parts 22.1, 22.2, 22.3 and/or the person 22, a movement pattern is created 80 for the body parts 22.1, 22.2, 22.3 and/or the person 22 in the next step. In a comparison step 82, the movement patterns can be compared with a priori information, for example previously learned movement patterns such as a gait pattern or gestures of the person 22, and an output 84a, 84b of the result of the comparison step 82 can be made on the basis of the comparison. Due to the high spatial resolution of the FMCW LiDAR sensor 12, for example, micro-movements of the chest can be detected as a characteristic feature of a person 22. After the classification of the chest of one or more people 22 in the monitoring area 16, the respiratory rate can be determined as a characteristic feature of a person 22, for example by means of spectral estimation, and can be used both for identification and for vitality control.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• DE 102007007576 A1 [0004]
• DE 19843602 A1 [0005]
• US 9804576 B2 [0006]
• DE 102006048163 B4 [0007]

Claims (10)

Device (10) for securing a surveillance area (16) with - at least one FMCW LiDAR sensor (12) for emitting transmitted light beams (14, 14.1, 14.2, 14.3,14.n) in the surveillance area (16), for scanning a Multiple measurement points (20, 20.1, 20.2, 20.3, 20.n) and for generating measurement data (18) from the measurement points (20, 20.1, 20.2, 20.3, 20.n) remitted or reflected transmitted light, and - a control - and evaluation unit (32) for evaluating the measurement data (18) and for generating a safety-related signal based on the evaluation, characterized in that the measurement data (18) radial speeds (v ^r _n ) of the measuring points (20, 20.1, 20.2, 20.3, 20.n), and the control and evaluation unit (32) is designed to measure the measuring points (20, 20.1, 20.2, 20.3, 20.n) using the radial speeds (v ^r _n ) of the measuring points (20, 20.1, 20.2, 20.3, 20.n) and to form objects (22, 24, 26, 28, 30) and/or object segments ( 22.1, 22.2, 22.3, 24.1, 24.2, 24.3). device after claim 1 , The control and evaluation unit (32) being designed to determine the radial speeds of the objects (22, 24, 26, 28, 30) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3) and characteristics of the Objects (22, 24, 26, 28, 30) and/or object segments using the radial velocities of the objects (22, 24, 26, 28, 30) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3) to extract (48). device after claim 2 , wherein the control and evaluation unit (32) is designed to the objects (22, 24, 26, 28, 30) and / or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3) using the radial speeds of the objects and/or to classify (50) object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3). Device according to one of Claims 1 until 3 , wherein the control and evaluation unit (32) is designed to measure the measuring points (20, 20.1, 20.2, 20.3, 20.n) using the radial speed (v ^r _n ) of the measuring points (20, 20.1, 20.2, 20.3, 20 .n) to filter. device after claim 4 , wherein the control and evaluation unit (32) is designed to discard measuring points (20, 20.1, 20.2, 20.3, 20.n) with a radial speed (v ^r _n ) below a predetermined threshold value for the evaluation. Apparatus according to any preceding claim, wherein the FMCW LiDAR sensor (12) is stationary. device after claim 6 , wherein the device has at least one additional FMCW LiDAR sensor with an additional monitoring area and the monitoring area (16) at least partially overlaps with the additional monitoring area. Device according to one of Claims 1 until 5 , wherein the FMCW LiDAR sensor (12) is movable. device after claim 8 wherein the FMCW LiDAR sensor (12) is mounted on a robotic arm (24.1, 24.2, 24.3). device after claim 8 wherein the FMCW LiDAR sensor (12) is mounted on an automated guided vehicle (26).

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