DE202022104108U1 - Device for detecting objects in a surveillance area - Google Patents

Abstract

Device (10, 90) for detecting objects (22, 24, 30, 96, 98, 100) in a monitoring area (16, 106).
- at least one FMCW LiDAR sensor (12, 92) for emitting transmitted light beams (14, ..., 14.n, 102.1, ..., 102.n) into the monitoring area (16, 106), for scanning a Large number of measuring points (20.1, ..., 20n, 104.1, ..., 104.n) and for generating measurement data (18) from the measuring points (20.1, ..., 20n, 104.1, ..., 104 .n) remitted or reflected transmitted light, the measurement data (18) comprising radial velocities (v ^r _n ) of the measuring points (20.1, ..., 20n, 104.1, ..., 104.n), as well as
- a control and evaluation unit (32) for evaluating the measurement data (18), characterized in that the FMCW LiDAR sensor (12, 92) is set up to generate polarization-dependent intensities (I _⊥n , I _∥n ,) of the To record remitted or reflected transmitted light at measuring points (20.1, ..., 20n, 104.1, ..., 104.n), the measurement data (18) include the polarization-dependent intensities (I _⊥n , I _∥n ,), and the tax - and evaluation unit (32) is designed to measure the measuring points (20.1, ..., 20n, 104.1, ..., 104.n) using the radial velocities (v ^r _n ) and the polarization-dependent intensities (I _⊥n , I _∥n ,) to segment and combine into objects (22, 24, 30, 96, 98, 100) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2). .

Description

The invention relates to a device for detecting objects in a surveillance area according to the preamble of claim 1.

Optoelectronic sensors such as laser scanners or 3D cameras are often used to detect objects, for example 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 device standard IEC61496 or EN61496 for non-contact protective devices (ESP). To meet these safety standards, a number of measures must be taken, such as secure electronic evaluation through redundant, diverse electronics, function monitoring or special monitoring of contamination of optical components.

In the DE 10 2007 007 576 A1 A machine is secured by a large number of laser scanners taking a three-dimensional image of its workspace 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 installed in a fixed location 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 US 9,804,576 B2 discloses a detection-based, industrial automation control that is set up to detect people's movements, derive them for the future and 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 the people's movements.

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

Another area of application for object detection using optoelectronic sensors is in the area of transport infrastructure, in particular infrastructure-to-vehicle (I2V) communication. In order to provide meaningful data, the data recorded by the optoelectronic devices can be segmented in various ways. “Semantic segmentation” refers to the assignment of the measurement points recorded by the optoelectronic sensors to individual semantic classes. Semantic classes can be so-called “things” (objects with a clearly defined shape, such as a car or a person) or so-called “things” (amorphous background regions, such as a road or the sky). Instance segmentation refers to the assignment of measurement points to object instances as well as to a predefined set of object classes (car 1, car 2, pedestrian 1, etc.). However, instance segmentation only takes “things” into account. “Things” are not classified as part of instance segmentation. This limitation of instance segmentation is removed with panoptic segmentation. Here both “things” and “things” are divided into instances and classes.

The image capture systems commonly 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 usually based on direct light transit time measurement. A light pulse is emitted by the sensor, reflected on an object and 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 using 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 the case of 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, trade-offs arise between maximum range, minimum remission of the object, integration time and the requirements for the signal-to-noise ratio of the sensor system. Incoherent radiation at the same wavelength (ambient light) also has a direct effect on the dynamic range of the light receiver. Examples of incoherent radiation at 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 light transit time measurement. With indirect time-of-flight measurement, the phase difference of an AMCW (Amplitude Modulated Continuous Wave) transmission signal and its time-delayed copy are determined after reflection with an object. The phase difference corresponds to the transit time of light and can be converted into a distance value using 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 measurement principle (FMCW) 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 therefore lower spatial resolution. In addition, regulatory regulations 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. Geometric physical features are therefore hardly usable in safety-related object and/or person detection compared to optical technologies.

In the EP 4 030 188 A1 The applicant proposes a device for detecting objects in a surveillance area, which has an FMCW LIDAR sensor as an optoelectronic sensor and, in addition to the usual parameters of location and intensity of a measuring point, the radial speed of the measuring point measured by the FMCW LIDAR sensor relative to the Sensor should be taken into account as an additional parameter when segmenting the measuring points. This allows for improved segmentation, particularly for objects with different radial velocities. However, if several objects move with the same or similar radial speed, this parameter can only contribute to a limited extent to the segmentation of the measuring points.

It is therefore the object of the invention to improve a device for detecting objects in a surveillance area with an FMCW LIDAR sensor.

This object is achieved by a device for detecting objects in a surveillance area according to claim 1.

The device according to the invention has at least one optoelectronic sensor, which is designed as a frequency modulated continuous wave (FMCW) LiDAR sensor and can be arranged, for example, on a machine, a vehicle or stationary. The basics of FMCW-LiDAR technology can be found, 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 the time-of-flight measurement of laser pulses, an FMCW LiDAR sensor does not emit pulsed, but continuous light beams into the monitoring area, which during a measurement, i.e. a discrete-time scanning of a measuring point in the monitoring area, produce a predetermined frequency modulation, i.e a temporal change in the wavelength of the transmitted light. The measurement frequency is typically in the range from 10 to 30 Hz. The frequency modulation can, for example, be designed as periodic up and down modulation. Transmitted light reflected or remitted from measuring points in the monitoring area has a time offset compared to the emitted transmitted light corresponding to the light transit time, which depends on the distance of the measuring point from the sensor and is accompanied by a frequency shift due to the frequency modulation. In the FMCW LiDAR sensor, emitted and reflected light is coherently superimposed, whereby the distance of the measuring point from the sensor can be determined from the superimposition signal. Compared to pulsed or amplitude-modulated incoherent LiDAR measuring principles, the measuring principle of coherent overlay has, among other things, the advantage of increased immunity to extraneous light 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 a person's geometric properties measurable.

If a measuring point moves towards or away from the sensor at a radial speed, the reflected transmitted light also has a Doppler shift. An FMCW LiDAR sensor can determine this change in the transmitted light frequency and use it to determine the distance and the radial speed of a measuring point in a single measurement, i.e. a one-time scanning of a measuring point, while a LiDAR sensor based on the time-of-flight measurement of laser pulses can determine the Radial velocity at least two measurements, i.e. two time-spaced scans of the same measuring point, are necessary.

The FMCW LiDAR sensor is also set up to detect polarization-dependent intensities of the transmitted light reflected or remitted by the measuring points. For this purpose, the FMCW LiDAR sensor has an outcoupling unit which is designed to detect at least part of the light emitted by the measuring points in the monitoring area reflected or remitted transmitted light, hereinafter also referred to as received light, and directed to a polarization analyzer, for example through a beam splitter with a predetermined division ratio.

The polarization analyzer is set up 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.

When scanning a three-dimensional monitoring area in discrete time and space, an FMCW LiDAR sensor according to the invention can thus record the following measurement data: $$M_{j,k,l}=\left(\begin{array}{l}{r}_{j,k,l}\\ v_{j,k,l}^r\\ I_{\perp j,k,l}\\ I_{\Vert j,k,l}\end{array}\right).$$

Here r _j,k,l denote the radial distance, V ^r j _,k,l the radial velocity and I _⊥j,k,l and l _∥j,k,l the polarization-dependent intensities of each spatially discrete measuring point j,k with a two-dimensional azimuth angle φ and polar angle θ specified position (φ _j , θ _k ) for each discrete-time sampling I. For easy readability, the index n is used below for a one-time discrete-time sampling of a spatially discrete, two-dimensional measuring point (φ _j , θ _k ) in the three-dimensional monitoring area.

To evaluate the measurement data recorded by the FMCW LiDAR sensor, the device according to the invention has a control and evaluation unit which is designed to segment the measuring points using the spatially resolved radial velocity of the measuring points and the polarization-dependent intensities of the transmitted light reflected or remitted by the measuring points and combine them into objects and/or object segments. Object segments are understood to be individually movable parts of an object consisting of several parts, for example the limbs of a human body, the components of a robot arm or the wheels of a vehicle.

The invention has the advantage that improved segmentation of the measurement data is possible by using the spatially resolved radial velocity and the polarization-dependent intensities of the transmitted light reflected or remitted from the measuring points as additional parameters. This also applies in particular to known segmentation methods in digital image processing or machine vision.

The control and evaluation unit can further 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 measures such as mean or standard deviation, higher moments, or histograms of the radial velocities 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 velocities 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 process the measurement data using the radial speed to filter the values of the measuring points. This means that the computational effort can be reduced through data reduction even before the measuring points are segmented. Filtering can be carried out, for example, by discarding measuring points with a radial velocity smaller, larger or equal to a predetermined threshold value and not subjected to any further evaluation. For example, in the case of an anti-collision function, objects and/or object segments that move with the sensor (v ^r = 0) or move away from the sensor (v ^r > 0) can be discarded.

The FMCW LiDAR sensor can be arranged stationary and scan a predetermined monitoring area. Preferably, at least one further FMCW LiDAR sensor can be provided, which scans a further monitoring area, whereby the monitoring areas can overlap. This makes it possible to avoid shadows or blind spots in which object detection is not possible. If two or more FMCW LiDAR sensors are arranged relative to one another in such a way that measurement beams that are orthogonal to one another can be generated, a velocity vector of an object scanned by these measurement beams can be determined by calculating these pairs of measurement beams in the plane spanned by the measurement beams that are orthogonal to one another.

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 can carry out movements using different axes and joints. The sensor can then follow the machine's movements and scan a changing monitoring area.

The sensor can preferably be designed to be safe in accordance with the standards mentioned in the introduction or comparable standards. 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 dangerous machine part, such as a tool tip. For example, if it is a robot with many axes, their interaction is not relevant to the sensor, as the sensor simply tracks the resulting movement at the location of the danger.

In a further 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 where a specific determination of 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 does not move with the machine, can also monitor the machine.

In one embodiment of the invention, the device can be set up for traffic monitoring, wherein the control and evaluation device can be designed to assign the measuring points to vehicle categories using the measurement data, in particular the radial velocities and the polarization-dependent intensities, and to evaluate them in a vehicle category-specific manner, for example for monitoring of vehicle category-specific speed restrictions (e.g. 80 km/h for a truck and 120 km/h for a car.

In one embodiment of the invention, the device can be set up to measure speeding at low speeds, in particular at speeds below 30 km/h.

In one embodiment of the invention, the device can be set up for license plate recognition of vehicles, whereby if the spatial resolution of the FMCW LIDAR sensor is sufficient, the control and evaluation device can be designed to assign a license plate number of a vehicle without further camera-based sensors by segmenting the measuring points capture. In an alternative embodiment for license plate recognition of vehicles, the control and evaluation device can be designed to trigger a camera if the spatial resolution of the FMCW LIDAR sensor is insufficient and to set its optimal integration time according to the vehicle speed in order to generate an optimal camera image.

In a further embodiment, the device can be set up to measure a traffic flow, wherein the control and evaluation unit is designed to determine a measure of the traffic flow by segmenting the measuring points into dynamic and static objects, for example by defining a static area such as a roadway as a 2D or 3D “Region of Interest” (ROI) in the monitoring area of the sensor and calculating an average radial velocity of all measurement points within this ROI. The traffic flow measurement can be coupled with a classification of the segmented objects in order to determine the traffic flow separately for different classes of road users.

In a further embodiment, the device can be set up for tracking and trajectory prediction of road users, wherein the control and evaluation unit is designed to assign measurement points to road users, to calculate trajectory predictions of the road users using the radial speeds of the measurement points assigned to the road users and to send the trajectory predictions to autonomous vehicles for driving decision making. If the 3D speed vectors of road users are known, either by calculating the measured radial speeds of orthogonal FMCW-LIDAR measuring beam pairs or by fusing data from other sensor modalities such as radar, camera or LIDAR sensors, these can further improve the trajectory prediction.

• 1 ein Beispiel für eine Radialgeschwindigkeitsmessung mit einem FMCW-LiDAR-Sensor;
• 2 eine schematische Darstellung einer erfindungsgemäßen Vorrichtung zur Überwachung eines Roboters;
• 3 eine schematische Darstellung einer erfindungsgemäßen Vorrichtung zur Verkehrsüberwachung;
• 4 ein Ablaufdiagram für eine beispielhafte erfindungsgemäße Verarbeitung von Messdaten eines FMCW-LiDAR-Sensors;
• 5 ein beispielhaftes Ablaufdiagramm zur Überwachung einer Bewegung eines Roboters;
• 6 ein beispielhaftes Ablaufdiagramm zur Vermeidung einer Kollision zweier Fahrzeuge 96, 98 im 12V Umfeld;

The invention will be explained in more detail below with regard to further features and advantages using exemplary embodiments and with reference to the accompanying drawing. The illustrations in the drawing show:

• 1 an example of a radial velocity measurement with an FMCW LiDAR sensor;
• 2 a schematic representation of a device according to the invention for monitoring a robot;
• 3 a schematic representation of a traffic monitoring device according to the invention;
• 4 a flowchart for an exemplary processing according to the invention of measurement data from an FMCW LiDAR sensor;
• 5 an exemplary flowchart for monitoring movement of a robot;
• 6 an exemplary flowchart for avoiding a collision between two vehicles 96, 98 in the 12V environment;

In 1 the concept of radial velocity measurement with an FMWC LIDAR sensor 12 is shown 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, measure the radial speed v ^r of the measuring point 20 of the object 38 in the direction of the FMCW LiDAR sensor 12. The FMCW LiDAR sensor 12 additionally has a polarization analyzer (not shown), which is set up to measure polarization-dependent intensities I _⊥, I _∥ of the transmitted light beam 14 reflected or remitted by the measuring point 20.

This information (radial distance r, radial velocity v ^r , polarization-dependent intensities I _⊥, I _∥ ) is available directly with a measurement, i.e. a discrete-time sampling of the measuring point 20. In order to identify moving objects, 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 are equivalent to.

In the case of a static FMCW LiDAR sensor, each measuring point with a radial velocity of zero is usually assigned to a static object, as long as it does not move tangentially to the sensor's measuring beam. Due to the finite object extent and the high spatial resolution of the FMCW LiDAR sensor, practically every moving object will have at least one measuring point 20 with a non-zero radial velocity v ^r _n to the FMCW LiDAR sensor 12. Therefore, a measurement from the FMCW LiDAR sensor 12 can be used to 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 move away can be discarded. Corresponding data reduction reduces the computational effort required for further evaluation of the measurement data.

2 shows a schematic representation of a device 10 according to the invention for monitoring a robot 24. An FMCW LiDAR sensor 12 sends out transmitted light beams 14.1, ..., 14.n into a three-dimensional monitoring area 16 and generates measurement data M _n 18 from measuring points 20.1, . .., 20.n reflected or remitted transmitted light in the monitoring area 16 back to the FMCW LiDAR sensor 12. For illustration purposes, a limited number of exemplary transmitted light beams 14.1, ..., 14.n and measuring 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 measuring points 20.1, ..., 20.n can be monitored people 22, robots 24, or boundaries of the surveillance area such as floors 30 or walls.

The measurement data M _n 18 of the FMCW LiDAR sensor 12 received by the control and evaluation unit 32 include, for each discrete-time sampling, the radial distances r _n of the measuring points 20.1, ..., 20.n from the FMCW LiDAR sensor 12, which polarization-dependent intensities I _⊥n , I _∥n of the transmitted light reflected or remitted from the measuring points 20.1, ..., 20.n and the radial velocities v ^r _n of the measuring points 20.1, ..., 20.n, where with radial velocity v ^r _n the speed component of a measuring point 20.1, ..., 20.n is referred to, with which the measuring point 20.1, ..., 20.n moves towards or away from the 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 measure the measuring points 20.1, ..., 20.n using the radial velocities v ^r _n of the measuring points 20.1, ... ., 20.n and the polarization-dependent intensities I _⊥n , I _∥n , of the transmitted light reflected or remitted from the measuring points 20.1, ..., 20.n and to form object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3 and/or objects. Based on the detection, the control and evaluation unit 32 can generate a safety-related signal to trigger a safety-related action. The safety-related action can be, for example, the activation of a warning light 34 or the stopping of 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, so it triggers the safety-related action itself. Alternatively, the control and evaluation unit 32 can pass on 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.

3 shows a schematic representation of a device 90 according to the invention for traffic monitoring. An FMCW lidar sensor 92 is arranged on a so-called toll or traffic sign bridge 94 in order to detect vehicles, in this case a truck 96 and a car 98, on a roadway 100. The FMCW LiDAR sensor 92 sends out transmitted light beams 102.1, ..., 102.n into a three-dimensional monitoring area 106 of the sensor 92 and generates measurement data from measuring points 104.1, ..., 104.n in the monitoring area 106 reflected back to the sensor 92 or remitted transmitted light. The FMCW LiDAR sensor 92 is arranged above or to the side of the roadway 100 to be monitored so that both vehicles 96, 98 are detected by the FMCW LiDAR sensor 92 at the same time, i.e. during a discrete-time scanning of the monitoring area 106 by the FMCW LiDAR sensor 92 , to be recorded.

The FMCW LiDAR sensor 92 is designed to detect polarization-dependent intensities I _⊥n , I _∥n of the transmitted light reflected or remitted by the measuring points 104.1, ..., 104.n, so that the FMCW LiDAR sensor 92 generated measurement data radial distances r _n and radial velocities v ^r _n of the measuring points 104.1, ..., 104.n as well as the polarization-dependent intensities I _⊥n , I _∥n , of the transmitted light reflected or remitted by the measuring points 104.1, ..., 104.n include, whereby radial speed ^{v r} _n denotes the speed component of a measuring point 104.1, ..., 104.n, with which the measuring point 104.1, ..., 104.n moves towards or from the FMCW LiDAR sensor 92 LiDAR sensor 92 moved away.

The measurement data are evaluated by a control and evaluation unit (not shown), the control and evaluation unit being set up to measure the measuring points 104.1, ..., 104.n using the radial velocities v ^r _n of the measuring points 104.1, ... , 104.n and the polarization-dependent intensities I _⊥n , I _∥n , of the transmitted light reflected or remitted from the measuring points 104.1, ..., 104.n and into object segments, in this case vehicle parts 96.1, 96.2, 96.3, 98.1 , 98.2, and/or objects, in this case vehicles 96, 98.

For vehicles 96, 98 traveling side by side at the same speed, the measured radial speed v ^r _n of the measuring points 104.1, ..., 104.n will not differ or only differ insignificantly. By using the polarization-dependent intensities I _⊥n , I _∥n of the transmitted light reflected or remitted from the measuring points 104.1, ..., 104.n, the segmentation of the measuring points 104.1, ..., 104.n can be improved, since the polarization-dependent intensities I _⊥n , I _∥n due to different surface properties of the object segments 96.1, 96.2, 96.3, 98.1, 98.2, and / or objects 96, 96 usually differ.

4 shows in a flow chart 42 an exemplary processing according to the invention of the measurement data recorded by the FMCW LiDAR sensor by the control and evaluation unit. After receiving 44 the measurement data, the measuring points 20.1, ..., 20.n, 104.1, ..., 104.n are segmented in a segmentation step 46 and formed into objects 22, 24, 96, 98, 100 and/or object segments 22.1 , 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 summarized, in addition to the 46 usually used for segmentation th spatial coordinates of the measuring points, the spatially resolved radial velocities v ^r _n of the measuring points 20.1, ..., 20.n, 104.1, ..., 104.n and the polarization-dependent intensities I _⊥n , I _∥n of the measuring points 104.1, .. ., 104.n reflected or remitted transmitted light are taken into account. Object segments can be, for example, individual movable components 24.1, 24.2, 24.3 of a robot 24, body parts 22.1, 22.2, 22.3 of a person 22 or vehicle parts 96.1, 96.2, 96.3, 98.1, 98.2.

• - Pixelorientierte Verfahren im Grauwertbild mittels Schwellenwert-Verfahren,
• - Kantenorientierte 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 value 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, the term “range segmentation” refers to special methods for segmenting three-dimensional data sets. “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 measuring points 20.1, ..., 20.n, 104.1, ..., 104.n, the segmentation 46 of the measuring points 20.1, ... , 20.n, 104.1, ..., 104.n can be done more efficiently and accurately using the methods listed above. For example, measuring points 20.1, ..., 20.n, 104.1, ..., 104.n with radial velocities v ^r _n smaller, larger or equal to a predetermined threshold value can be discarded and not sent to any further evaluation. In the case of an anti-collision function, for example, measuring 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 through 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 floors 30, roadways 100 or walls can be identified before or during the segmentation 46 of the measuring points 20.1, ..., 20.n, 104.1, ..., 104.n are discarded and the computing effort is reduced by data reduction. By using the polarization-dependent intensities I _⊥n , I _∥n of the transmitted light reflected or remitted from the measuring points 20.1, ..., 20.n, 104.1, ..., 104.n, the measuring points can be with 20.1, ..., 20.n, 104.1, ..., 104.n can also be segmented more precisely if they have similar or the same radial velocities v ^r _n .

In the next step, a feature extraction 48 of the objects 22, 24, 30, 96, 98 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 defined during the segmentation 46 takes place. Typical features that can be extracted from the objects 22, 24, 30, 96 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 when processing the measurement data are, for example Width, number of measurement points or length of the circumference of the objects and/or object segments, or other characteristics, 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). According to the invention, these features can be expanded to include features that are based on the radial velocities of the objects 22, 24, 30, 96 and/or the object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 . For this purpose, radial velocities of the objects and/or object segments are first determined, for example by applying trigonometric functions to the radial velocities of the measuring points representing the respective object and/or object segment. Additional object and/or object segment features can then be, for example, statistical measures of the radial velocities of the objects and/or object segments, such as mean values, standard deviations, higher moments or histograms, which are characteristic table for robot and/or person movements can be used.

After the feature extraction 48, a classification 50 of the objects 22, 24, 96 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 takes place using known classification methods such as Bayes classifiers, support Vector machines or artificial neural networks. Classification involves searching the feature space for groups of features that define an object. The above-listed statistical measures of the radial speed of individual objects 22, 24, 30, 96, 98, 100 and/or object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 in combination with a priori information is used to define feature spaces that can, for example, classify and thus distinguish people 22 or vehicles 96 based on their radial speed.

In a further step 52, a movement pattern of at least one of the object segments 22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2 is determined using the radial velocities of the measuring points 20.1, . assigned to the at least one object segment. .., 20.n, 104.1, ..., 104.n.

The result of the determination of the movement pattern 52 can be further processed by the control and evaluation unit 32 after the output 54, for example to generate a safety-related signal, to detect the status of an object segment or be passed on to a higher-level controller (not shown) via the interface 36.

5 shows an exemplary flowchart 54 for monitoring a movement of a robot. 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. For segments 24.1, 24.2, 24.3 of the robot arm identified in the classification 50, a determination 56 of representative variables such as radial distances and intensities takes place and radial velocities of segments 24.1, 24.2, 24.3.

• • 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.

beschrieben, können die gemessenen Radialgeschwindigkeitswerte direkt zur Erkennung eines Bewegungsmusters verwendet werden, so dass insbesondere zwei zeitlich aufeinanderfolgende Abtastungen M_n,l und M_n,l-1 des Überwachungsbereichs nicht zwingend notwendig sind. In einem Vergleichsschritt 60 erfolgt ein Vergleich des Bewegungsmusters 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.Based on the measured radial velocities of the measuring points assigned to the previously classified segments 24.1, 24.2, 24.3, a movement pattern 58 is recognized. In contrast to the typical determination of a “rigid scene flow” based on 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.

described, the measured radial velocity values can be used directly to detect a movement pattern, so that in particular two consecutive scans M _n,l and M _n,l-1 of the monitoring area are not absolutely necessary. In a comparison step 60, the movement pattern 58 is compared with a priori information about expected target 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, movement deviation beyond a predetermined tolerance level), a safety-related action 62, for example switching off the robot 24, is initiated.

6 shows an exemplary flowchart 66 for avoiding a collision between two vehicles 96, 98 in the I2V environment using a method according to the invention. After receipt 44 of the measurement data M _n from the FMCW LIDAR sensor 92, the steps of segmentation 46 of the measurement data, feature extraction 48 and classification 50 are carried out as described above in order to identify vehicle parts 96.1, 96.2, 96.3, 98.1, 98.2, and/or objects, and/ or to identify the vehicles 96, 98 themselves. In the following step, a movement forecast 68 of the vehicle parts 96.1, 96.2, 96.3, 98.1, 98.2, and/or the vehicles 96, 98 is carried out 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 their performance. From the predicted movements, for example, a Time-To-Collision (TTC) can be determined as a quantitative measure of the risk of collision 70 and, in the event of a negative comparison result, for example a risk of collision, a warning signal can be sent to the vehicles as part of Infrastructure to Vehicle (I2V) communication 96 , 98 are transmitted.

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed 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.

Cited patent literature

• DE 102007007576 A1 [0003]
• DE 19843602 A1 [0004]
• US 9804576 B2 [0005]
• DE 102006048163 B4 [0006]
• EP 4030188 A1 [0012]

Claims (10)

Device (10, 90) for detecting objects (22, 24, 30, 96, 98, 100) in a surveillance area (16, 106) with - at least one FMCW LiDAR sensor (12, 92) for emitting transmitted light beams ( 14, ..., 14.n, 102.1, ..., 102.n) in the monitoring area (16, 106), for scanning a large number of measuring points (20.1, ..., 20n, 104.1, ..., 104.n) and for generating measurement data (18) from transmitted light remitted or reflected from the measurement points (20.1, ..., 20n, 104.1, ..., 104.n), the measurement data (18) being radial velocities (v ^r _n ) of the measuring points (20.1, ..., 20n, 104.1, ..., 104.n), as well as - a control and evaluation unit (32) for evaluating the measurement data (18), characterized in that the FMCW LiDAR -Sensor (12, 92) is set up to measure polarization-dependent intensities (I _⊥n , I _∥n ,) of the transmitted light remitted or reflected by the measuring points (20.1, ..., 20n, 104.1, ..., 104.n). to record, the measurement data (18) include the polarization-dependent intensities (I _⊥n , I _∥n ,), and the control and evaluation unit (32) is designed to measure the measuring points (20.1, ..., 20n, 104.1,. .., 104.n) using the radial velocities (v ^r _n ) and the polarization-dependent intensities (I _⊥n , I _∥n ,) to segment and produce objects (22, 24, 30, 96, 98, 100) and/ or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2). Device according to one of the preceding claims, wherein the control and evaluation unit (32) is designed to measure the measuring points (20.1, ..., 20n, 104.1, ..., 104.n) using the polarization-dependent intensities (I _⊥n , I _∥n ,) of the transmitted light remitted or reflected from the measuring points (20.1, ..., 20n, 104.1, ..., 104.n) and/or the radial velocity (v ^r _n ) of the measuring points (20.1, .. ., 20n, 104.1, ..., 104.n). Device according to Claim 1 or 2 , wherein the control and evaluation unit (32) is designed to measure radial velocities of the objects (22, 24, 30, 96, 98, 100) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2 , 96.3, 98.1, 98.2) and characteristics of the objects (22, 24, 30, 96, 98, 100) and / or object segments using the radial velocities of the objects (22, 24, 30, 96, 98, 100) and /or to extract object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2) (48). Device according to Claim 3 , wherein the control and evaluation unit (32) is designed to control the objects (22, 24, 30, 96, 98, 100) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2) using the radial velocities of the objects (22, 24, 30, 96, 98, 100) and/or object segments (22.1, 22.2, 22.3, 24.1, 24.2, 24.3, 96.1, 96.2, 96.3, 98.1, 98.2) to be classified (50). Device according to one of the preceding claims, wherein the control and evaluation unit (32) is designed to measure points (20.1, ..., 20n, 104.1, ..., 104.n) with a radial speed (v ^r _n ) below one to discard the specified threshold value for the evaluation. Device according to one of the preceding claims, wherein the FMCW LiDAR sensor (12) is stationary. Device according to Claim 6 , wherein the device has at least one further FMCW LiDAR sensor with a further monitoring area and the monitoring area (16) at least partially overlaps with the further monitoring area. Device according to one of the Claims 1 until 5 , whereby the FMCW LiDAR sensor (12) is movable. Device according to Claim 8 wherein the FMCW LiDAR sensor (12) is attached to a robot arm (24.1, 24.2, 24.3). Device according to Claim 9 wherein the FMCW LiDAR sensor (12) is attached to a driverless transport vehicle (26).

Images