WO2024217953A1

WO2024217953A1 - Method for calibrating a sensor

Info

Publication number: WO2024217953A1
Application number: PCT/EP2024/059681
Authority: WO
Inventors: Florent Monay; Jérôme MAYE; Nicolas JACQUEMIN; Loic PERRUCHOUD
Original assignee: Ams Osram AG
Current assignee: Ams Osram AG
Priority date: 2023-04-20
Filing date: 2024-04-10
Publication date: 2024-10-24
Anticipated expiration: 2025-10-20

Abstract

In the method for calibrating a sensor, a set of trajectory parameters is obtained by moving the sensor with respect to a calibration surface, wherein the sensor is operated with a set of initial intrinsic parameters. The trajectory is fitted using the trajectory parameters and the initial intrinsic parameters of the sensor. Subsequently a calculated surface is fitted using the trajectory. In a further step, a set of updated intrinsic parameters is obtained from comparing the calculated surface and the calibration surface.

Description

METHOD FOR CALIBRATING A SENSOR

A method for calibrating a sensor is speci fied .

A task to be solved is inter alia to speci fy a method for calibrating a sensor that can be easily carried out in a short period of time .

These tasks are solved by a method comprising the features of independent claim 1 . Advantageous embodiments and further developments are the subj ect-matter of the respective dependent patent claims .

According least one embodiment , the method for calibrating a sensor comprises obtaining a set of traj ectory parameters by moving the sensor with respect to a calibration surface , wherein the sensor is operated with a set of initial intrinsic parameters . For example , the sensor is configured to detect and/or measure 3D obj ects , distances to obj ects or the like . In particular, the sensor is capable of measuring the calibration surface . For example , during intended operation a distance of the sensor to the calibration surface or the shape of the calibration surface may be obtained .

In particular, the calibration surface serves as a reference for the sensor . The traj ectory parameters may describe the movement of the sensor in a three-dimensional space in front of the calibration surface . While the set of traj ectory parameters is being obtained, the sensor is preferably in operation . That is , the traj ectory parameters are collected by the sensor during its intended operation . According to least one embodiment of the method or the preceding embodiment , a traj ectory using the traj ectory parameters and the initial intrinsic parameters of the sensor is fitted . In particular, the traj ectory parameters obtained by moving the sensor are used to obtain the traj ectory as a three-dimensional space curve . The traj ectory, in particular, describes the movement of the sensor in front of the calibration surface .

According to at least one embodiment of the method or at least one of the preceding embodiments , a calculated surface using the traj ectory is obtained . That is , a calculation is performed by which the calculated surface is obtained from the traj ectory . For example , by the traj ectory a point cloud may be calculated that gives a discreti zation of the calculated surface . The point cloud is preferably obtained in three dimensions . The point cloud can be considered as a construct that the sensor " sees" during operation and movement with respect to the calibration surface . That is , the point cloud describes the calibration surface as obtained by the sensor . The point cloud does in general not describe the calibration surface exactly . In particular, from the point cloud the calculated surface is obtained, for example by a constructive approximation or fitting .

According to at least one embodiment of the method or at least one of the preceding embodiments , a set of updated intrinsic parameters is obtained by comparing the calculated surface and the calibration surface . Since the calculated surface is obtained from the fitted traj ectory, which is itsel f obtained from the obtained traj ectory parameters and the initial intrinsic parameters , the calculated surface may deviate from the calibration surface . That is , the point cloud, respectively the calculated surface may not exactly describe the calibration surface . In other words , there is a mismatch between the results of the measurements carried out by the sensor during movement with respect to the calibration surface , i . e . , what the sensor " sees" , and the actual calibration surface . By comparing the calculated surface and the calibration surface , a deviation of these two surfaces can be obtained . The deviation is preferably minimi zed by adapting the intrinsic parameters . Thereby, the set of updated intrinsic parameters is obtained .

As an illustration, for example , the intrinsic parameters are varied in order to obtain a varied point cloud and thus a varied calculated surface which is again compared to the calculated surface . I f the deviation between the calculated surface and the calibration surface undergoes a certain criteria, variation of the intrinsic parameters is terminated . By this procedure the deviation of the calculated surface to the calibration surface may be minimi zed . In particular, the set of intrinsic parameters that have been varied j ust before the termination criteria is ful filled is the set of updated intrinsic parameters .

In at least one embodiment of the method for calibrating a sensor, a set of traj ectory parameters is obtained by moving the sensor with respect to a calibration surface , wherein the sensor is operated with a set of initial intrinsic parameters . The traj ectory is fitted using the traj ectory parameters and the initial intrinsic parameters of the sensor . Subsequently a calculated surface is obtained using the traj ectory . In a further step, a set of updated intrinsic parameters is obtained from comparing the calculated surface and the calibration surface .

The method for calibrating a sensor is based inter alia on the following technical considerations . Intrinsic parameters of a sensor, for example a 4D ranger sensor, might dri ft from its factory calibration over time . This dri ft will negatively impact the accuracy of the sensor and needs to be compensated for to achieve accurate measurements . Such recalibration can be performed, for example , by capturing radial distances to multiple planes that are extracted from a standard scene . Thereby, however, assumptions on a plurality of planes have to be done to calibrate the sensor which makes the calibration method complicated and computation-intensive .

Another possibility for performing a sel f-calibration is , for example , to maximi ze a point cloud quality by measuring a degree of organi zation of the point cloud and by expressing the degree of organi zation as a function of unknown intrinsic parameters . This however, require to capture a comparably large scene to obtain the point cloud with a density high enough . Also , approaches to recalibrate rotating LIDAR sensors are thinkable . However, in this approaches commonly only three rotational degrees of freedom may be obtained while three special degrees of freedom have to be determined by an optimi zation algorithm .

The method described herein makes use of the idea of moving the sensor in front of a single calibration surface . During this movement the sensor is operated as intended using initial intrinsic parameters . That is , the sensor is in particular adapted to measure the calibration surface and/or a distance to the calibration surface or the like . For example , in an application, the sensor may be connected to or integrated in a device . The sensor may be a so-called 4D ranger sensor . By the sensor the position and/or the velocity of the device in a three-dimensional space may be obtained and monitored . The device is , for example , a controlling device for video games , a mobile phone or the like .

Advantageously, the method described herein especially relies on the calibration surface that is preferably known . For example , the calibration surface is flat .

That is , the method is free of a step of detecting or tracking an obj ect or the like . This is in particular possible since the calibration surface is known . Therefore , it is in particular not required to track the position or geometry of the calibration surface . Hence , the method may not rely on a detection of targets in consecutive frames and there is no step for detection and tracking of obj ects in a scene . Therefore , the sensor does in particular not need to measure the same point on the calibration surface for twice a sensor calibration .

It is in particular possible to j ointly capture speed and velocity in a single frame when calibrating the sensor and/or during operation of the sensor . This is for example possible by using so-called sel f mixing interferometry ( SMI ) for determining a sensor signal . For generating a SMI signal , reflected laser radiation scattered by a target may be coupled into a laser oscillator used to generate emitted laser radiation . By superimposition of the scattered light and the emitted laser light in the oscillator, a signal may be observed by monitoring the power supply of the laser oscillator. This SMI signal may be used to simultaneously determine position and velocity of the target.

This is in contrast to common comparable sensors, in particular so-called time of flight (TOF) sensors. For example, a common TOF sensor allows for recalibration by detection and tracking of objects. For example, an object is tracked between different viewpoints and the relative 3D positions of the object is measured by the TOF camera. The trajectory of the TOF sensor itself, which may be a driving car or vehicle, is compared to these observations, i.e., the position of the object changing over time. Any mismatch may be used to fine tune or calibrate the TOF sensor or a set of TOF parameters .

In particular, a sensor by which the method described here may be carried out may use or require only a few sensing directions in a limited field of view. Due this sparsity, i.e., only a few directions with a very limited field of view per direction, the sensor may not be capable of detecting objects or points in a scene as common TOF sensors. However, since the method described herein can be carried out without tracking of objects, this is not necessarily required.

The initial intrinsic parameters may be decalibrated and need to be recalibrated in order to sustain the accuracy of the sensor. Thereby, the calibration surface serves as a constraint for the optimization problem of the intrinsic parameters. For solving the optimization problem a calculated surface is obtained by the sensor using the trajectory parameters and the initial intrinsic parameters. For example, the sensor is used as intended and the calibration surface is measured. However, due to decalibration of the initial intrinsic parameters , the calculated surface di f fers from the calibration surface . By minimi zing this di f ference the updated intrinsic parameters may be obtained . In other words , a variational problem is solved, wherein the intrinsic parameters serve as variational parameters while the updated intrinsic parameters are the variational parameters at the minimum . This makes the calibration method described herein a simple and quick procedure that can be conducted without a speci fic test setup . This means that advantageously no complicated testing environment or calibration environment is needed for recalibration of the sensor .

According to at least one embodiment of the method or at least one of the preceding embodiments , the calibration surface is a plane surface . Preferably the calibration surface is flat . The calibration surface may be a wall or a table or the like . By using a planar wall as the calibration surface , the capturing of the traj ectory parameters can easily be carried out . In particular, no speci fic calibration environment needs to be provided .

For example , the calibration surface defines a reference coordinate system comprising an x-axis , a y-axis and a z-axis orthogonal to each other . Preferably, the reference coordinate system is a cartesian coordinate system . In case that the calibration surface is a plane surface , the plane surface defines a xy-plane . The xy-plane is in particular a plane defined by the x-axis and the y-axis . That is , the x- axis and the y-axis lie inside the xy-plane . The z-axis is preferably orthogonal to the xy-plane . For example , the z- axis is parallel to a normal vector of the xy-plane . According to at least one embodiment of the method or at least one of the preceding embodiments , a set of updated intrinsic parameters is obtained by minimi zing a cost function that includes calculating a di f ference between the calculated surface and the calibration surface . The cost function can be used as a measure on how large the deviation of the calculated surface is from the calibration surface . The cost function may involve additional terms in addition to the di f ference between the calculated surface and the calibration surface . In general the cost function is a nonlinear function . For example , obtaining the updated intrinsic parameters is solving a variational problem, where the cost function is the function to be minimi zed, the intrinsic parameters are the variational parameters and the updated intrinsic parameters are the parameters at the minimum . Thereby, the calibration surface may serve as a constraint . That is , i f the collection of traj ectory data would have been carried out with the updated intrinsic parameters , the fitted calculated surface would be essentially identical to the calibration surface .

According to at least one embodiment of the method or at least one of the preceding embodiments , the cost function is minimi zed by means of a non-linear optimi zation . For example , the non-linear optimi zation provides a scheme according to which the above-mentioned variational problem, where the cost function serves as the function to be minimi zed, may be solved . The non-linear optimi zation problem may be implemented using a library such as the Ceres Solver Library . An output of the non-linear optimi zation are preferably the updated intrinsic parameters . According to at least one embodiment of the method or at least one of the preceding embodiments , the sensor includes an inertia measurement unit . The traj ectory parameters include data from the inertia measurement unit . For example , kinematic degrees of freedom may be obtained by the inertia measurement unit . To achieve this , the inertia measurement unit preferably includes at least one acceleration sensor, preferably three acceleration sensors orthogonal to each other . Furthermore , the inertia measurement unit may comprise three gyroscopic sensors which are preferably orthogonal to each other . Rotational motion may be measured by the gyroscopic sensors .

According to at least one embodiment of the method or at least one of the preceding embodiments , moving the sensor includes pitching, yawing and rolling the sensor with respect to the calibration surface . For example , pitching is a rotational motion around the x-axis , rolling is a rotational motion around the z-axis and yawing is a rotational motion around the y-axis . By pitching, yawing, and rolling the sensor with respect to the calibration surface , three degrees of freedom of the sensor may be obtained .

The movements pitching, yawing and rolling are preferably obtained by the inertia measurement unit . That is , the data associated with these kinds of motion are data from the inertia measurement unit . In particular, these data are obtained by three orthogonal gyroscopic sensors of the inertia measurement unit .

According to at least one embodiment of the method or at least one of the preceding embodiments , the sensor comprises at least one light source with at least one beam direction . For example , the light source is a semiconductor laser such as a laser diode . The light source is configured to emit electromagnetic radiation . For example , electromagnetic radiation in the blue or green or red spectral range or in the UV range or in the IR range is emitted by the light source during intended operation . Preferably, electromagnetic radiation in the infrared wavelength range is emitted by the light source in intended operation . The sensor in particular further comprises at least one optic, such as a lens , to shape and direct light of the light source in beam direction .

According to at least one embodiment of the method or at least one of the preceding embodiments , the sensor comprises a plurality of beam directions . For example , the sensor comprises an optic by which radiation emitted by the lightsource is send into a plurality of directions . Alternatively or additionally, the sensor may comprise a plurality of light sources , each having a beam direction .

According to at least one embodiment of the method or at least one of the preceding embodiments , the initial intrinsic parameters comprise an initial beam direction and the updated intrinsic parameters comprise an updated beam direction . The initial beam direction and the updated beam direction may di f fer from each other . For example , due to a thermal dri ft of the optic of the sensor, the beam direction may change over time . In particular, the optic of the sensor may misalign over time due to thermal influences or a mechanical impact , making a recalibration necessary . That is , the beam direction may decalibrate . Therefore , a recalibration of the beam direction may be needed . According to at least one embodiment or at least one of the preceding embodiments , the sensor is a LIDAR sensor operated in FMCW mode . LIDAR is an acronym for Light Detection and Ranging and FMCW is an acronym for Frequency Modulated Continuous Wave . In this case , a frequency of the continuous wave emitted by the sensor is modulated by a chirp signal via an modulation . Preferably, the chirp signal is of comparably low power . Radiation of the sensor including the chirp signal is reflected by a target , thereby undergoing a frequency shi ft . The reflected signal is detected by the sensor to determine the distance to the target . By applying a conversion factor to the observed frequency shi ft , the distance to the target may be calculated . I f the target also has a radial velocity, a Doppler-shi f t is added to the reflected signal . This additionally allows to determine the velocity of the target .

According to at least one embodiment of the method or at least one of the preceding embodiments , the initial intrinsic parameters comprise an initial conversion factor and the updated intrinsic parameters comprise an updated conversion factor . The initial conversion factor and the updated conversion factor may di f fer from each other . Since a distance from the sensor to a target is obtained by means of the conversion factor, a di f ference in initial conversion factor and the updated conversion factor requires a recalibration of the sensor .

According to at least one embodiment of the method or at least one of the preceding embodiments , the traj ectory parameters comprise parameters optically obtained by the sensor . That is , the respective traj ectory parameters may be collected by operating the sensor as intended . According to at least one embodiment or at least one of the preceding embodiments , the traj ectory parameters include a radial velocity and a radial distance of the sensor with respect to the calibration surface . The radial velocity describes in particular the velocity of the sensor with respect to the surface . Both the radial velocity and the radial distance of the sensor with respect to the calibration surface are preferably obtained by operating the sensor as intended . That is , in the case that the sensor is a LIDAR operated in FMCW mode , the target from which information is collected is the calibration surface .

In particular, when moving the sensor with respect to the calibration surface , a plurality of radial distances and/or radial velocities is obtained, from which the traj ectory may be calculated . I f , for example , the sensor has a plurality of beam directions , the determination of the radial distance and/or radial velocity at each point in time may be carried out by utili zing the di f ferent beam directions , thereby increasing the accuracy of the measurement , since information of a plurality of beams is combined .

In particular, in intended operation the calibration method obtains a point cloud and thus the calculated surface from information collected when measuring the calibration surface . This calculated surface may di f fer from the calibration surface which allows for an, in general non-linear, optimi zation of the intrinsic parameters wherein the calibration surface serves as a constraint .

According to at least one embodiment of the method or at least one of the preceding embodiments , the traj ectory is a 6DOF traj ectory . The term " 6DOF" refers to six degrees of freedom . In particular, the six degrees of freedom are the six degrees of freedom in which a rigid body can move in a three-dimensional space . These may be referred to as upwards- downwards , backwards- forwards , right and left , rolling, pitching, yawing . For example , upwards-downwards is a movement along the y-axis , backwards- forwards is a movement along the z-axis and right and left is a movement along the x-axis of the reference coordinate system . Rolling, pitching and yawing are preferably movements as discussed above .

The six degrees of freedom are preferably collected by the inertia measurement unit and the traj ectory parameters obtained by using the sensor as intended . By obtaining a 6DOF traj ectory, the movement of the sensor in the three- dimensional space can be advantageously described unambiguously .

According to at least one embodiment of the method or at least one of the preceding embodiments , obtaining of the set of updated intrinsic parameters is carried out by a calculator unit of the sensor . Preferably, the calculator unit is integrated in the sensor . Advantageously, no further calculator device is needed i f the calculator unit is integrated in the sensor . This makes the method to be carried out particularly easy .

According to at least one embodiment of the method or at least one of the preceding embodiments , moving the sensor is carried out by a user of the sensor . Because no complicated testing environment or calibration environment is needed for recalibration of the sensor according to the method described herein, the recalibration can be performed by a user of the sensor . For example , the sensor is used in a mobile phone , a controlling device for video games or the like .

In the case of a decalibration of the intrinsic parameters , the user may perform the method described herein on their own and recalibrate the device for the application .

According to at least one embodiment , the method is free of a step of tracking the calibration surface . This is in particular possible since the calibration surface is known . Therefore , it is in particular not required to track the position or geometry of the calibration surface . Hence , the method may not rely on a detection of targets in consecutive frames and there is no step for detection and tracking of obj ects in a scene . Therefore , the sensor does in particular not need to measure the same point on the calibration surface for twice a sensor calibration .

Further advantages and advantageous embodiments and further developments of the method described herein will become apparent from the following exemplary embodiments shown in connection with schematic drawings . Identical elements , elements of the same kind or elements having the same ef fect , are provided with the same reference signs in the figures . The figures and the proportions of the elements shown in the figures are not to be regarded as true to scale . Rather, individual elements may be shown exxageratedly large for better representability and/or for better comprehensibility .

In the figures : Figures 1 and 2 show a schematic illustration a method for calibrating a sensor described herein according to an exemplary embodiment ;

Figure 3 shows a sensor used in the method according to the exemplary embodiment in a schematic illustration .

Figure 1 illustrates the collecting of traj ectory parameters

2 by moving a sensor 1 with respect to a calibration surface

3 along a sensor movement traj ectory 10 . The calibration surface 3 is plane surface , for example a wall . The calibration surface 3 defines a reference coordinate system x, y, z . The calibration surface 3 lies in a xy-plane defining an x-axis and a y-axis of the reference coordinate system x, y, z . The z-axis is perpendicular to the calibration surface 3 .

For illustration three positions 11 , 12 , 13 of the sensor 1 are shown . The sensor 1 is moved from the first position 11 to the second position 12 to the third position 13 . Additionally to the translational movement of the sensor 1 between the positions 11 , 12 , 13 the sensor 1 is pitched, yawed, rolled with respect to the calibration surface . The translational movement is a movement in the three-dimensional space defined by the reference coordinate system x, y, z . Pitching of the sensor 1 is a rotational movement around the x-axis , rolling is a rotational movement around the z-axis in time is a rotational movement around the y-axis . By the translational movement of the sensor 1 between the positions 11 , 12 , 13 and the rotational movements around the x- , y- , and z-axis all degrees of freedom the sensor can move in a three-dimensional space are covered . To detect in particular the rotational movements pitching, yawing and rolling, the sensor 1 comprises an inertia measurement unit 8 . The inertia measurement unit 8 comprises a plurality of acceleration sensors and a plurality of gyroscopic sensors to detect motion of the sensor 1 .

The sensor 1 comprises a light source 15 and an optic 16 ( compare figure 3 ) . The light source 15 is a semiconductor laser, for example a laser diode . The optic 16 is lens or a lens array . During intended operation the light source 15 emits radiation in the infrared wavelength range of the electromagnetic spectrum . This radiation is formed and directed by the optic 16 in beam direction 41 . In the present embodiment the sensor 1 emits radiation in a plurality of beam directions 41 .

The sensor 1 is a LIDAR sensor which is operated in FMCW mode . That is , by modulating the frequency of radiation emitted by the sensor 1 with a chirp signal a radial velocity 21 and a radial distance 22 with respect to the calibration 3 may be obtained by the sensor 1 during operation . Thereby the emitted radiation is reflected at a target , i . e . , the calibration surface 3 , and the reflected radiation is detected by the sensor 1 . The reflected radiation shows a frequency shi ft with respect to the emitted radiation . This frequency shi ft can be evaluated by converting the frequency shi ft to a distance by means of a conversion factor . The usage of several beam directions 41 increases the accuracy of the measurement of the sensor 1 since data from a plurality of beams can be evaluated .

While moving the sensor 1 with respect to the calibration surface 3 the sensor 1 is operated as intended . That is , the sensor 1 measures and evaluates the radial velocity 21 and the radial distance 22 with respect to the calibration surface 3 . Thereby, the sensor 1 is operated with a set of initial intrinsic parameters 4 . The initial intrinsic parameters 4 may dri ft over time resulting in an inaccurate measurement of the sensor 1 . Therefore , a recalibration of the initial intrinsic parameters 4 is necessary . The intrinsic initial parameters 4 comprise an initial beam direction 41 and an initial conversion factor 43 . For example , the optic 16 of the sensor 1 may misalign over time due to thermal influences or a mechanical impact , making a recalibration necessary .

Figure 2 illustrates the recalibration of the intrinsic parameters according to the method described herein . During the method for calibration, traj ectory parameters 2 are captured, for example by moving the sensor 1 with respect to the calibration surface 3 along the sensor movement traj ectory 10 as shown in figure 1 . The traj ectory parameters 2 comprise data 6 of the inertia measurement unit 8 , the radial velocity 21 and the radial distance 22 .

Together with the initial intrinsic parameters 40 , which comprise the initial beam directions 41 and the initial conversion factor 43 , a traj ectory 20 is fitted . The traj ectory 20 is a 6DOF traj ectory, wherein 6DOF refers to six degrees of freedom . Hence , the traj ectory 20 describes all six degrees of freedom for the movement of the sensor 1 in a three-dimensional space . This is in particular possible since movement in the rotational degrees of freedom are captured by the inertial measurement unit 8 . The calculated traj ectory 20 may be identical or di f fer from the sensor movement trajectory 10 because of a decalibration of the initial intrinsic parameters 4.

Subsequently, a calculated surface 30 is calculated by using the trajectory 20 and the initial intrinsic parameters 4. This is possible since the trajectory 20 as well as the radial distance 22 to the calibration surface 3 is known. However, the calculated surface 30 differs from the calibration surface 3 because of decalibration of the initial intrinsic parameters 4. For example, a 3D point cloud of the results of a measurement of the calibration surface 3 by the sensor 1 is evaluated. That is, the point cloud is what the sensor 1 "sees" when moving along the sensor movement trajectory 10. From the point cloud the calculated surface 30 may be obtained, for example, by approximation.

In a next step, a cost function 5 is calculated. The cost function 5 is in general a non-linear function. The cost function 5 includes a difference between the calculated surface 30 and the calibration surface 3. This difference may be regarded as a measure for the decalibration of the sensor 1. By minimizing the cost function 5, i.e., the deviation between the calculated surface 30 and calibration surface 3, a set of updated intrinsic parameters 40 may be obtained. Thereby, the sensor 1 is calibrated, respectively recalibrated .

Minimization of the cost function 5 is carried out by a nonlinear optimization 50. For the non-linear optimization 50 the Ceres Solver Library may be used. The non-linear optimization 50 in particular solves a variational problem, wherein the cost function 5 is the function to be minimized, the initial intrinsic parameters 4 are the variation of parameters , the updated intrinsic parameters 40 are the variation of parameters at the minimum . Furthermore , the calibration surface 3 , which is a planar surface , serves as a constraint to the variational problem . That is , i f the sensor 1 would be operated with the updated intrinsic parameters 40 during moving the sensor 1 along the sensor movement traj ectory 10 , the calculated surface 30 would essentially match the calibration surface 3 .

The method of the exemplary embodiment of figure 2 is preferably carried out by a calculator unit 7 of the sensor 1 . The calculator unit 7 is integrated in the sensor 1 . That is , no further calibration device or calibration environment is needed to calibrate the sensor 1 . This makes the method for calibrating the sensor 1 easy to be carried out .

Preferably, movement of the sensor 1 is performed by a user of the sensor 1 or any device that includes the sensor 1 . Such a device may be a mobile phone or a controlling device , for example for a video game , or the like .

Figure 3 illustrates in a schematic view the sensor 1 used in the method for calibrating a sensor according to the exemplary embodiment . The sensor 1 comprises the calculator unit 7 , the inertia measurement unit 8 and at least one light source 15 in a housing 17 . At an edge of the housing, the optic 16 is arranged . In intended operation the light source

15 produces radiation in the infrared wavelength range of the electromagnetic spectrum, which is emitted through the optic

16 in beam directions 41 .

The light source 15 is a semiconductor laser . For example , the light source 15 is a laser diode . The light source 15 preferably comprises a semiconductor layer sequence including an active zone . During operation, electromagnetic radiation is produced in the active zone . For example , the semiconductor layer sequence of the light source is based on an I I I-V compound semiconductor material .

The optic 16 may comprise a lens , a lens array and/or a metalens structure . The optic 16 may be integrated in the housing 17 so that at least a part of an outer surface of the housing 17 is formed by the optic 16 .

The inertia measurement unit 8 comprises three gyroscopic sensors which are orthogonal to each other . Rotational motion such as pitching, yawing and rolling of the sensor 1 is captured by the gyroscopic sensors . Furthermore , the inertia measurement unit 8 may comprise at least one acceleration sensor, by means of which translational motion along the x- , y- , and/or z-axis may be detected . The inertia measurement unit 8 is connected to the calculator unit 7 so that data 6 of the inertia measurement 8 can be used for calibration of the sensor 1 .

The calculator unit 7 may comprise a computer . For example , the calculator unit 7 comprises a microcontroller, an AS IC or the like . AS IC is short for application-speci fic integrated circuit . The calculator unit is in particular configured to fit the traj ectory 20 , to calculate the calculated plane 30 and to perform the non-linear optimi zation 50 ( compare figure 2 ) .

The invention is not restricted to the exemplary embodiments by the description on the basis of said exemplary embodiments . Rather, the invention encompasses any new feature and also any combination of features which in particular comprises any combination of features in the patent claims and any combination of features in the exemplary embodiments , even i f this feature or this combination itsel f is not explicitly speci fied in the patent claims or exemplary embodiments .

This patent application claims the priority of German patent application 102023110066 . 1 , the disclosure content of which is hereby incorporated by reference .

References

1 sensor

2 traj ectory parameters

3 calibration surface

4 initial intrinsic parameters

5 cost function

6 inertia measurement unit data

7 calculator unit

8 inertia measurement unit

10 sensor movement traj ectory

11 first position

12 second position

13 third position

15 light source

16 optic

17 housing

20 traj ectory

21 radial velocity

22 radial distance

30 calculated surface

40 updated intrinsic parameters

41 initial beam direction

42 updated beam direction

43 initial conversion factor

44 updated conversion factor

50 non-linear optimi zation x, y, z reference coordinate system

Claims

Claims :

1. Method for calibrating a sensor (1) comprising:

- Obtaining a set of trajectory parameters (2) by moving the sensor (1) with respect to a calibration surface

(3) , wherein the sensor (1) is operated with a set of initial intrinsic parameters (4) ,

- Fitting a trajectory (20) using the trajectory parameters (2) and the initial intrinsic parameters (4) of the sensor ( 1 ) ,

- Obtaining a calculated surface (30) using the tra j ectory (20) ,

- Obtaining a set of updated intrinsic parameters (40) from comparing the calculated surface (30) and the calibration surface (3) .

2. Method according to claim 1, wherein the calibration surface (3) is a plane surface.

3. Method according to one of the preceding claims, wherein obtaining a set of updated intrinsic parameters (40) comprises minimizing a cost function (5) including calculating a difference between the calculated surface (30) and the calibration surface (3) .

4. Method according to the preceding claim, wherein the cost function (5) is minimized by means of non-linear optimization .

5. Method according to one of the preceding claims, wherein - the sensor (1) includes an inertia measurement unit

( 8 ) , and the trajectory parameters (2) include data (6) from the inertia measurement unit.

6. Method according to one of the preceding claims, wherein moving the sensor (1) includes pitching, yawing, and rolling the sensor (1) with respect to the calibration surface ( 3 ) .

7. Method according to one of the preceding claims, wherein the sensor (1) comprises at least one light source (15) with at least one beam direction (41, 42) .

8. Method according to claim 7, wherein the sensor (1) comprises a plurality of beam directions (41, 42) .

9. Method according to claim 7 or 8, wherein the initial intrinsic parameters (4) comprise an initial beam direction (41) and the updated intrinsic parameters (40) comprise an updated beam direction (42) .

10. Method according to claim one of claims 7 to 9, wherein

- the sensor (1) is a LIDAR sensor operated in FMCW mode, and

- the initial intrinsic parameters (4) comprise an initial conversion factor (43) and the updated intrinsic parameters (40) comprise an updated conversion factor (44) .

11. Method according to one of claims 7 to 10, wherein the trajectory parameters (2) comprise parameters optically obtained by the sensor (1) .

12. Method according to claim 11, wherein the trajectory parameters (2) include a radial velocity (21) and a radial distance (22) of the sensor (1) with respect to the calibration surface (3) .

13. Method according to one of the preceding claims, wherein the trajectory (20) is a 6DOF trajectory.

14. Method according to one of the preceding claims, wherein obtaining the set of updated intrinsic parameters (4) is carried out by a calculator unit (7) of the sensor (1) .

15. Method according to one of the preceding claims, wherein moving the sensor (1) is carried out by a user of the sensor ( 1 ) .

16. Method according to one of the preceding claims, wherein the method is free of tracking the calibration surface