阅读说明：本技术 用于光学距离测量的方法及设备 (Method and device for optical distance measurement ) 是由 H·霍尔朱特 于 2020-03-26 设计创作，主要内容包括：提出了用于光学距离测量的方法(100),该方法包括执行(101)至少一个飞行时间测量,其中飞行时间测量包括借助于发射单元(12)发射(102)至少一个测量脉冲,反射(103)至少一个发射的测量脉冲,并借助于接收单元(11)接收(104)至少一个反射的测量脉冲。方法(100)包括基于飞行时间测量生成(105)反向散射曲线(20),并评估(109)反向散射曲线(20)以用于物体识别(113)。方法(100)进一步包括提供(106)用于评估(109)反向散射曲线(20)的灵敏度曲线(21),其中,评估(109)包括确定(110)灵敏度曲线(21)与反向散射曲线(20)之间的相关性,以便使用至少一个飞行时间测量来确定粒子云是否布置在通过飞行时间测量所测量的测量范围内,并向飞行时间测量分配(120)粒子云特征。(A method (100) for optical distance measurement is proposed, the method comprising performing (101) at least one time-of-flight measurement, wherein the time-of-flight measurement comprises transmitting (102) at least one measurement pulse by means of a transmitting unit (12), reflecting (103) the at least one transmitted measurement pulse, and receiving (104) the at least one reflected measurement pulse by means of a receiving unit (11). The method (100) comprises generating (105) a backscatter curve (20) based on the time-of-flight measurements, and evaluating (109) the backscatter curve (20) for object identification (113). The method (100) further comprises providing (106) a sensitivity curve (21) for evaluating (109) the backscatter curve (20), wherein evaluating (109) comprises determining (110) a correlation between the sensitivity curve (21) and the backscatter curve (20) for determining whether the particle cloud is arranged within a measurement range measured by the time-of-flight measurement using at least one time-of-flight measurement, and assigning (120) a particle cloud characteristic to the time-of-flight measurement.)

1. Method (100) for optical distance measurement,

wherein the method (100) comprises performing (101) at least one time-of-flight measurement,

wherein the time-of-flight measurement comprises: transmitting (102) at least one measurement pulse by means of a transmitting unit (12), reflecting (103) the at least one transmitted measurement pulse, and receiving (104) the at least one reflected measurement pulse by means of a receiving unit (11),

wherein the method (100) comprises generating (105) a backscatter curve (20) based on the time-of-flight measurement,

wherein the method (100) comprises evaluating (109) the backscatter curve (20) for object identification (113),

it is characterized in that

The method (100) comprises providing (106) a sensitivity curve (21) for evaluating (109) the backscatter curve (20),

wherein the evaluating (109) comprises determining (110) a correlation between the sensitivity curve (21) and the backscatter curve (20) for determining, using the at least one time-of-flight measurement, whether a particle cloud (27) is arranged within a measurement range measured by the time-of-flight measurement, and assigning (120) a particle cloud characteristic to the time-of-flight measurement.

2. The method (100) of claim 1,

it is characterized in that

Determining (110) the correlation comprises considering possible scaling of the backscatter curve (20) as a function of the density of the particle cloud (27).

3. The method (100) of claim 1 or 2,

it is characterized in that

In order to provide (106) the sensitivity curve (21), the method (100) comprises simulating (107) the sensitivity curve (21).

4. Method (100) according to one of the preceding claims,

it is characterized in that

To provide (106) the sensitivity curve (21), the method (100) comprises measuring (108) the sensitivity curve (21).

5. The method (100) of claim 4,

it is characterized in that

The method (100) comprises using the device (10) for optical distance measurement,

wherein the method comprises measuring (108) a sensitivity curve (21) specific for the device (10).

6. Method (100) according to one of the preceding claims,

it is characterized in that

The method (100) comprises identifying (114) a particle cloud (27), and preferably determining (114a) a confidence in the identification (114) of the particle cloud (27).

7. The method (100) of claim 6,

it is characterized in that

The method (100) comprises performing a plurality of time-of-flight measurements,

wherein the state of the particle cloud measured by the time of flight is 'yes' or 'no' respectively,

wherein the method comprises setting a first threshold value,

wherein a particle cloud is identified (114) if the number of time-of-flight measurements for which the particle cloud status is "yes" exceeds a first threshold.

8. The method (100) according to one of claims 5 or 6.

It is characterized in that

The method (100) comprises performing a plurality of time-of-flight measurements,

wherein the method comprises setting a first threshold value,

wherein the method comprises setting a second threshold for the particle cloud probability,

wherein a particle cloud is identified (114) when the number of time-of-flight measurements for which the particle cloud probability is higher than the second threshold exceeds the first threshold.

9. The method (100) according to one of claims 6 to 8.

It is characterized in that

The method (100) comprises identifying (115) a density of the identified particle cloud (27), and preferably determining (115a) a confidence for the density identification (115).

10. The method (100) according to one of claims 6 to 9.

It is characterized in that

The method (100) comprises outputting (117) a message about the identified particle cloud (27) and preferably about a confidence of the particle cloud (27) identification, and/or outputting a message about a density of the identified particle cloud (27), and preferably about a confidence of the density identification.

11. Method (100) according to one of claims 6 to 10,

it is characterized in that

The method (100) comprises determining a confidence level of the time-of-flight measurement taking into account the identification (114) of the particle cloud (27).

12. Method (100) according to one of the preceding claims,

it is characterized in that

The method (100) comprises object recognition (113),

wherein the method comprises distinguishing (118) between the object (26) and the particle cloud (27).

13. Method (100) according to one of the preceding claims,

it is characterized in that

The receiving unit (11) comprises a plurality of receiving elements,

wherein the method (100) comprises providing (106) a sensitivity curve for each receiving element.

14. Device (10) for optical distance measurement,

wherein the device comprises a receiving unit (11) and a transmitting unit (12) for performing (101) at least one time-of-flight measurement,

it is characterized in that

The device (10) is configured for implementing a method according to one of claims 1 to 13.

15. Computer program product comprising a computer readable medium, on which a program is stored, which, once loaded into the memory of a computer, enables the computer to carry out the method (100) according to any one of claims 1 to 13, in conjunction where necessary with the device (10) according to claim 14.

16. Computer-readable storage medium, on which a program is stored, which, once loaded into the memory of a computer, enables the computer to carry out the method according to any one of claims 1 to 13, in conjunction where necessary with a device (10) according to claim 14.

Technical Field

The invention relates to a method and a device for optical distance measurement.

Background

LIDAR ("acronym for light detection and ranging") sensors are known from the prior art. The LIDAR sensor comprises a transmitting unit for transmitting a measurement pulse and a receiving unit for receiving a reflected measurement pulse, which is reflected by an object within a measurement range of the sensor. According to the time-of-flight principle, the speed of light is used to infer the distance to the object reflecting the measurement pulse.

What is obtained by such a time-of-flight measurement is a point cloud, in which each point represents a single measurement value, i.e. the reception of the transmitted and reflected measurement pulses. Generally, the quality of such a point cloud can be affected in bad weather conditions, such as fog, dust, spray, etc. This is because the reflection of the particle and its accompanying point in the LIDAR point cloud is erroneously identified as an object reflection. Depending on the particle density, complete damage to the device may eventually result.

The devices for distance measurement known from the prior art do not themselves recognize that they are located in the particle cloud. As a result, the apparatus erroneously recognizes the particle as an object. This reduces the quality of the time-of-flight measurement. However, since the device cannot recognize this situation, time-of-flight measurements that have been degraded by the particle cloud are processed with the same certainty (i.e., with the same high confidence) as measurements made without the particle cloud. Due to the time-of-flight measurements made in the subsequent calculation units, which evaluate the transmitted data over several time intervals, only the quality of the resulting point cloud can be estimated.

Summary of The Invention

It is an object of the invention to further develop a method and a device for optical distance measurement such that the reflection of a measurement pulse on a particle cloud can be distinguished from the reflection on an object.

The aforementioned object is achieved by a method for optical distance measurement comprising performing at least one time-of-flight measurement.

The time-of-flight measurement comprises transmitting at least one measurement pulse, in particular precisely one or more measurement pulses, by means of a transmitting unit, and preferably reflecting at least one transmitted measurement pulse, in particular a transmitted measurement pulse, on the particle cloud or on at least one object, and receiving at least one measurement pulse, in particular a reflected measurement pulse, by means of a receiving unit. In particular, the method includes generating a point cloud based on the time-of-flight measurements. The point cloud is the result of a time-of-flight measurement.

The method may further preferably comprise determining the time of flight of each transmitted, reflected and received measurement pulse. In particular, the method further comprises generating a histogram in which the received optical power (or a corresponding electronic signal based on the measured optical power) is recorded with respect to the determined time of flight. The determined time of flight of several measurement pulses may be further averaged. A histogram with the mean value may then also be generated.

In particular, each reflected measurement pulse received by means of the receiving unit generates a point in the point cloud, preferably in a 2D or 3D local coordinate system. Thus, as a result of the time-of-flight measurement, at least one point in the point cloud is obtained. In addition, the determined time of flight of several received and reflected measurement pulses may be averaged such that the latter together represent one point in the point cloud.

The method comprises generating a backscatter curve based on the time-of-flight measurement, wherein the method comprises evaluating the backscatter curve for object identification of at least one object and/or at least one particle cloud onto which the at least one measurement pulse is reflected. The method comprises providing a sensitivity curve for evaluating the backscatter curve, wherein evaluating the backscatter curve comprises determining a correlation between the sensitivity curve and the backscatter curve to determine whether a particle cloud is disposed through a measurement range measured in at least one time-of-flight measurement using the at least one time-of-flight measurement, and assigning a particle cloud characteristic to the time-of-flight measurement. In particular, the term "measurement range of the measurement" is to be understood as the area over which the measurement pulse of the time-of-flight measurement passes.

A correlation is determined in order to determine whether the particle cloud is located within a measurement range measured by means of at least one time-of-flight measurement. In other words, it is determined whether the device for implementing the method is located in and/or before the particle cloud. The method advantageously comprises using at least one time-of-flight measurement to determine whether a particle cloud is disposed within the measurement range, and assigning a particle cloud characteristic to the time-of-flight measurement.

In particular, the term "particle cloud characteristics" is to be understood as a particle cloud state and/or a particle cloud probability. In particular, a particle cloud characteristic is determined based on the determined correlation. The particle cloud status may indicate whether the correlation indicates that the particle cloud is within the measurement range. Thus, the status may be displayed as a double yes/no message. For example, if the correlation exceeds a preset threshold, the status may be "yes", and if the correlation is below this threshold, the status is "no".

Additionally, a particle cloud probability may be determined. The particle cloud probability can be understood as a confidence in the fact that the measurement pulse is reflected on the particle cloud. As a result, the method can determine a confidence between 0 and 100%, where 100% indicates a very high correlation, thus assuming that the measurement pulse is reflected on the particle cloud, and 0% indicates a nearly negligible correlation, thus assuming that no measurement pulse is reflected on the particle cloud. In particular, a confidence is determined based on the determined correlation.

In particular, the correlation is determined so as not to falsely identify the particle cloud as an object.

The term "optical distance measurement" is to be understood as an optical signal, here an optical measurement pulse, for determining the distance. The distance covered by a measurement pulse is understood to be the path between the transmitting unit that transmits the measurement pulse and the reflecting object that reflects the measurement pulse, plus the path between the reflecting object and the receiving unit that receives the corresponding reflected measurement pulse. The reflected measuring pulses each represent a backscatter signal of the emitted measuring pulse. The term "reflective object" may be understood as an object and/or a particle cloud.

In particular, the method is used in unmanned navigation of vehicles, in particular automobiles. Most importantly, the method can be used to autonomously control a vehicle. Additionally, the method may assist the driver of the vehicle. For this purpose, in particular the distances to all reflective objects located within the measuring range are determined.

In particular, the measurement pulse is an optical signal, in particular an electromagnetic signal. Advantageously, the measurement pulse is an optical pulse, i.e. a pulse having a wavelength not originating from the visible range of the human eye. For security reasons, invisible infrared light is preferably used. The measuring pulse preferably has a pulse width such that the measuring pulse can be understood as a temporally limited portion of the electromagnetic radiation. Since the measurement pulse comprises an electromagnetic signal and the velocity of the measurement pulse is known, the speed of light can be used to deduce from the time of flight of the measurement pulse which path the measurement pulse covers within that time of flight.

The receiving unit may preferably comprise at least one detector, for example a photodetector, in particular an optical detector. The receiving unit may comprise a plurality of receiving elements, which preferably operate in a linear or Geiger mode. The receiving element operating in linear mode may in particular comprise an avalanche photodiode, a PN photodiode, a PIN diode or a photomultiplier, while the receiving element operating in geiger mode preferably comprises a Single Photon Avalanche Diode (SPAD). In the latter, each photon is measured separately and averaged through the obtained histogram. In particular, time-dependent single photon counting is used. The term "receiving element" especially comprises a pixel. When the receiving element is used in the geiger mode, it is preferable not to use an a/D converter. This reduces the cost of the process.

Furthermore, the emission unit can comprise a plurality of emission elements, which are formed in particular by lasers. In particular, a transmit unit is to be understood as a "transmit matrix", i.e. an array of transmit elements, and a receive unit is to be understood as a receive matrix, i.e. an array of receive elements. In particular, a matrix is understood to be a three-dimensional, in particular plate-shaped, body on the surface of which the respective elements, i.e. the transmitting or receiving elements, are arranged.

In particular, the term "particle cloud" includes the accumulation of particles in air. In particular, the particle cloud comprises an aerosol. Advantageously, the particle cloud is a finely divided solid and/or liquid particle suspended in air. For example, the particle cloud may be finely dispersed droplets in air, such as a mist or spray. The particle cloud may also be a finely divided solid dust particle, such as dust or smoke. In addition, a particle cloud is understood to mean an exhaust gas cloud from an exhaust pipe of a vehicle, which consists of, in particular, oil and soot. The size of the particles, in particular the diameter, is usually less than 1mm, in particular less than 100 μm, most preferably less than 50 μm.

In contrast to particle clouds, objects are not aerosols in the sense of the present invention. Primarily solid or liquid.

The term "backscatter curve" is to be understood as a curve which can be used to infer the optical power received by the receiving unit in relation to the determined time of flight. In particular, the backscatter curve is a histogram of the generated time-of-flight measurements. The received optical power is preferably converted into an electrical signal, which is recorded with respect to the determined time of flight. In order to generate the backscatter curve, the respective electronic signal and the time of flight of the at least one transmitted and re-received measurement pulse are preferably determined on the basis of the measured optical power, wherein the at least one respective value pair is recorded on a coordinate system with the electronic signal as y-axis and the time of flight as x-axis.

In other words, the backscatter curve comprises the raw signal of the time-of-flight measurement. It is understood that the original signal is an analog or digital representation of the reflected light signal, in other words an electronic signal into which the received reflected light power is converted. The backscatter curve is the result of all transmitted, reflected and re-received measurement pulses of the same time-of-flight measurement.

In the case of the use of receiving elements operating in linear mode, the time-of-flight measurement comprises, in particular, the precise emission of a measurement pulse. The time-of-flight information of the multiple photons has been taken into account during the reception of the respectively emitted and reflected measurement pulses, so that the generated histogram of the single measurement pulse represents the backscatter curve. This is due to the fact that a single photon is reflected on a closer particle of the particle cloud while other photons are reflected on a more distant particle.

In contrast, when the receiving element is used in geiger mode, the time-of-flight measurement comprises the emission of a plurality of measurement pulses, wherein the histogram shows the average time-of-flight. The histogram may represent a backscattering curve.

The term "sensitivity curve" of the receiving unit is to be understood in particular as a sensitivity curve of the receiving unit, in particular of an apparatus for carrying out the method. The sensitivity curve describes, among other things, how sensitive the receiving unit or the individual receiving elements of the receiving unit are to the reaction of the magnitude of the distance between the reflecting object and the receiving unit. In other words, the sensitivity curve describes the effect of the distance of the reflecting object on which the measurement pulse is reflected on the detected optical power, assuming the same optical power of the measurement pulse. In addition, other factors may also affect the sensitivity curve, such as differences between the transmitting and receiving units of the device and/or the distance of the transmitting and receiving units from each other. The basic influencing factors on the sensitivity curve also include the optical and mechanical design of the receiving and/or transmitting unit and the electronic components used (for example receiving and/or transmitting elements, in particular diodes, and/or amplifiers). The sensitivity curve is thus receiver-unit-specific, in particular device-specific or sensor-specific.

In general, the sensitivity curve represents the elevation, i.e., the "peak," in a coordinate system in which optical power is converted to electronic power on the y-axis and time-of-flight is on the x-axis, preferably with rising and falling edges.

The shape of the sensitivity curve comes from the fact that: as the distance increases, it is naturally desirable that the optical power of the backscattered signal becomes smaller. However, due to the above-mentioned influencing factors, the maximum power is only reached starting at a certain distance from the receiving unit.

The method may comprise providing a single shared sensitivity curve for the entire receiving unit and/or the entire device. In addition, the method may include providing a sensitivity curve for each receive element of the receive unit. The method may advantageously generate a backscatter curve for each individual receiving element. In other words, a receive element may be assigned to each time-of-flight measurement. The correlation of each receiving element may be determined based on the backscatter curve and the sensitivity curve, and may optionally be averaged.

In addition, only a part of the receiving elements or only one receiving element of the receiving unit may be used for particle cloud identification. In other words, the number of receiving elements used for particle cloud detection is less than the total number of receiving elements.

The receiving element is then used to determine a backscatter curve and a sensitivity curve.

The evaluation includes determining a correlation between the sensitivity curve and the backscatter curve. It is advantageous here that the sensitivity curve has very similar properties to the section of the backscatter curve that can be attributed to reflection on the particle cloud.

Described in simplified terms, this is due to the fact that the particle cloud extends over a certain distance range to the apparatus for carrying out the method, so that a number of measurement pulses will be reflected onto closer particles of the particle cloud, while other measurement pulses will be reflected onto more distant particles of the particle cloud. In general, reflections are typically received from all distances enclosed by the particle cloud to the apparatus used to implement the method. Precisely, this type of reflection is represented by a sensitivity curve reflecting how the receiving unit (preferably the device for implementing the method) responds to reflections from all varying distances from the receiving unit (in particular the device for implementing the method).

The order of the sensitivity curve is therefore very similar to the section of the backscatter curve due to reflection on the particle cloud. In particular, the shape of the curves is similar. In particular, the determination of the correlation between the sensitivity curve and the backscatter curve makes it possible to determine whether at least one section of the backscatter curve has a similar order, in particular a shape, to the sensitivity curve, such that this section will be due to reflections on the particle cloud.

The term "correlation" is to be understood as the relation between the backscattering curve and the sensitivity curve. The correlation is at least one section of the backscattering curve, in particular how similar its shape is to the sensitivity curve. This section of the backscattering curve may correspond to a reflection on the particle cloud.

The backscatter curve first comprises at least one elevation, preferably a plurality of elevations. In particular, an elevation exists if it significantly protrudes from the noise of the backscatter curve, in other words from the corresponding deviation of the values of the optical power with the same time of flight. An elevation is present if it has a maximum optical power, preferably corresponding to at least twice the noise.

In particular, a correlation is determined to confirm whether at least one section of the backscattering curve exhibits similarity to the sensitivity curve. In particular, the term "segment" is to be understood as a part of the backscattering curve, which includes the elevation in the backscattering curve. In particular, it may be confirmed based on the determined correlation whether a particle cloud is measured during the time-of-flight measurement, in other words whether a measurement pulse of the time-of-flight measurement is reflected on the particle cloud.

The method may comprise identifying a particle cloud and preferably determining a confidence level for identifying the particle cloud. In particular, a confidence is determined based on the determined correlation. Confidence is the degree of quality (in other words, confidence) of the particle cloud identification.

The method preferably comprises outputting a message regarding at least one particle cloud characteristic. For example, the output may be as follows: the particle cloud state is "yes" and the particle cloud probability is 75% ".

In particular, the method comprises performing a plurality of time-of-flight measurements, wherein the particle cloud state of the time-of-flight measurements is "yes" or "no", respectively. The method includes setting a first threshold, wherein a particle cloud is identified if the number of time-of-flight measurements having a particle cloud status of "yes" exceeds the first threshold. The first threshold may be defined as "1", for example such that a particle cloud has been identified in a given time of flight measurement and the particle cloud status is "yes". In addition, the first threshold may be defined in such a way that it measures 10%, most preferably 20%, of all time-of-flight measurements.

Additionally, the method may include setting a second threshold for particle cloud probability, wherein a particle cloud is identified when the number of time-of-flight measurements for which the particle cloud probability is higher than the second threshold exceeds the first threshold.

In particular, confidence is expressed as a probability. The method therefore determines a confidence between 0 and 100%, where 100% represents a very high correlation and therefore the method determines that the measurement pulse is reflected on the particle cloud, whereas 0% represents a negligible correlation and therefore it must be assumed that no measurement pulse is reflected on the particle cloud.

The method preferably comprises outputting a message regarding the identification of the particle cloud and the determined confidence level. For example, the output may be as follows: "particle cloud is identified here with 75% confidence".

In particular, the correlation is determined by folding the backscatter curve with the sensitivity curve. Further, the determination of the correlation may comprise a fourier transform, in particular a fast fourier transform. The determination of the correlation may further comprise using an optimal filter tuned to the sensitivity curve. It can be trained beforehand, wherein the method comprises training of the optimal filter by means of a neural network. In particular, the optimal filter can be trained with the help of an auto-encoder. In addition, the optimal filter can be trained by deep learning.

To provide a sensitivity curve, the method may comprise simulating the sensitivity curve.

Alternatively or additionally, a sensitivity curve may be measured. For example, at least one object may be placed at different distances from the receiving unit, in particular in the absence of a particle cloud, wherein the receiving unit receives the respective reflected optical power and converts it into a corresponding electronic signal. The receiving unit or rather the device for processing comprises biaxial optics. Thus, overall, this produces a series of electrical signals that are a measure of the reflected light power as a function of distance or time of flight, and hence the sensitivity curve. By comparison with simulations, the advantage of the measurement, in particular the end point of its measurement, is that it also includes the electrical properties of the receiving unit, in particular of the apparatus used for carrying out the method, in other words the electrical properties of the sensors, and the mechanical and optical deviations of the individual sensors. In addition, the simulation requires more effort and may be incomplete.

The sensitivity curve can be measured specifically for the device (in other words, the sensor) designed to perform the method. The term "sensor design" is to be understood in the first place as the design of the sensor, influenced by the model and the corresponding technology, or by the detector type and/or the optics and/or the measurement principle.

In particular, the method comprises taking into account possible scaling of the backscattering curve as a function of the density of the particle cloud when determining the correlation. In particular, the reflection on the particle cloud depends on its thickness. The denser the particle cloud, the higher the signal (i.e. indirect optical power) in the backscatter curve. In other words, the backscattering curve can be scaled by a factor that takes into account the density of the particle cloud. The denser the particle cloud, the more the backscatter curve is compressed in the x-axis direction. Such scaling may change the progress of the backscatter curve with respect to the sensitivity curve. Although the elevation in the backscatter curve due to reflections on the particle cloud is highly dependent on its thickness, the basic shape still resembles the sensitivity curve to some extent, even though this is still true by particle clouds of different densities, a correlation can be determined which indicates that a reflection is a reflection on a particle cloud.

In another step, the method may include identifying a density of the particle cloud, and preferably determining a confidence level for identifying the density. In particular, the method may comprise taking into account the height of the peak. The evaluation preferably comprises an estimation of the maximum height in the backscatter curve allocated to the elevation of the reflection on the particle cloud, from which the density of the particle cloud can be deduced. Confidence is a measure of the quality (in other words, confidence) of the identified density.

In particular, the confidence for identifying the density is expressed as a probability. The confidence determined by the method is therefore 0 to 100%, where 100% means that the evaluation makes it very clear that the identified density can be deduced, and the method therefore makes it very certain that the particle cloud happens to have this density, while 0% means that the evaluation is unclear, and therefore it is very uncertain whether the particle cloud really has the identified density.

The method preferably includes outputting a message regarding the identification of the density and the determined confidence level. For example, the output may be as follows: "density of particle cloud measures X particles per volume with a confidence of 80%".

The message may be initially directed to a driver of the vehicle, wherein the vehicle is navigated by means of the method according to the invention and/or the driver is assisted by means of the method according to the invention. The output is particularly used to inform the driver that the vehicle is located in and/or traveling towards the particle cloud.

In particular, the method may comprise performing at least one additional measurement to measure measurement data of at least one additional sensor modality. In other words, the sensor modality relates to the sensor class. Thus, the sensors differ in particular with regard to the sensor modality or sensor class. In particular, the sensor modality determines the measurement method that produces the measurement data. The sensor modality preferably comprises lidar, radar, image or ultrasound. With respect to the respective sensor modality, this preferably means relating to a lidar sensor, a radar sensor, an image sensor, in particular a camera, or an ultrasound sensor. The method preferably comprises measuring radar measurement data, image data and/or ultrasound data.

The method may preferably comprise determining a confidence level for detecting the particle cloud of the measurement data of each sensor modality, wherein the method further comprises balancing the determined confidence levels. In particular, an overall confidence is determined, wherein the confidence of the individual sensor modalities is taken into account. The overall confidence may be output with the message.

In particular, a respective time-of-flight measurement may be assigned to each point in the point cloud. In a further step, the method may comprise determining a confidence of the time-of-flight measurement, in particular of the corresponding point in the point cloud, by taking into account the identification of the particle cloud.

In particular, the confidence may be understood as the quality of the time-of-flight measurement, i.e. as the probability of a degradation of the quality or of its results, in particular by means of reflections on the particle cloud. The higher the confidence level, the less likely it is to degrade. Overall, the method thus makes it possible to estimate the quality of the time-of-flight measurements.

Once the particle cloud is identified, the confidence of the time of flight measurements degraded by the particle cloud is adjusted. In particular, the confidence is adjusted compared to time of flight measurements during which no particle cloud is identified. In particular, the confidence is determined from the identification of the particle cloud and/or the identification of its density and/or the confidence for the identification of the particle cloud and/or the confidence of the identification density.

Additionally, the method may comprise generating a 3D depth map describing distances and/or directions from the device for implementing the method to the particle cloud and/or the identified object.

If the method comprises a plurality of time-of-flight measurements, the various steps described above are preferably performed for each measurement.

The confidence of the time-of-flight measurements may be first assigned to the corresponding points in the point cloud. The method may include generating a point cloud comprising points from various time-of-flight measurements. Assignment of confidence levels allows the point cloud to contain points with various confidence levels.

In particular, the method comprises object recognition, wherein the method comprises distinguishing between the object and the particle cloud. In particular, elevations in the backscatter curve are observed and classified within the framework of object recognition. In order to evaluate whether an object should be assigned to an elevation in the backscatter curve, information about the similarity (i.e. the correlation between the backscatter curve and the sensitivity curve) may be relevant. If there is sufficient correlation between the elevation and the sensitivity curve, it must be assumed that the elevation is not caused by reflections on the object, but on the particle cloud. The method first includes classifying elevations as particle clouds or objects, and the method also preferably includes determining a confidence level for each classification. In particular, a confidence is determined based on the determined correlation. Confidence is a measure of the quality of the classification, in other words, the confidence of the classification.

In particular, confidence is expressed as a probability. Thus, the method determines a confidence level between 0 and 100%, where 100% indicates a correlation that is very clear indicating that the corresponding classified reflecting object is involved here, and 0% indicates that the correlation is unclear, thus it is uncertain whether the elevation is due to a reflection on the particle cloud or on the object.

The method advantageously comprises outputting a message regarding the classification and the determined confidence level. For example, the output may be as follows: "here an object is identified at distance x with a 60% confidence".

In another aspect, the invention includes an apparatus for distance measurement, wherein the apparatus comprises a receiving unit and a transmitting unit for performing at least one time-of-flight measurement. The transmitting unit is used for transmitting at least one measuring pulse, in particular a plurality of measuring pulses, and the receiving unit is used for receiving at least one reflected measuring pulse, in particular a reflected measuring pulse, which has been transmitted in advance and reflected on the particle cloud or at least one object. The device is designed to implement the above-described method.

In particular, the invention also comprises an evaluation unit which is designed to evaluate the backscatter curve for object identification, in particular for the identification of at least one object and/or one particle cloud on which the measurement pulse is reflected. The evaluation unit is further designed to determine a correlation between the sensitivity curve and the backscatter curve. The evaluation unit may further be designed to recognize the object and/or to recognize the particle cloud and/or to recognize the object and/or to classify the density of the recognized particle cloud and/or to output a response and/or to determine a confidence and/or to distinguish the particle cloud from the object. The evaluation unit may comprise a digital signal processor, such as an ASIC and/or an FPGA and/or a microprocessor or the like.

The device comprises first a LIDAR device, in particular a scanning LIDAR device or a flash LIDAR device. In particular, the device comprises a LIDAR sensor. The device may further comprise a radar sensor, an image sensor, in particular a camera, or an ultrasound sensor. In addition, the device may be a driver assistance system.

In another aspect, the invention comprises a computer program product consisting of a computer readable storage medium on which a program is stored which, once loaded into the memory of a computer, enables the computer to carry out the above method, possibly in combination with the above apparatus.

Furthermore, the invention relates to a computer-readable storage medium, on which a program is stored, which, once loaded into the memory of a computer, enables the computer to carry out the above-mentioned method, if necessary in combination with the above-mentioned apparatus.