WO2019105737A1 - Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet - Google Patents

Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet Download PDF

Info

Publication number
WO2019105737A1
WO2019105737A1 PCT/EP2018/081222 EP2018081222W WO2019105737A1 WO 2019105737 A1 WO2019105737 A1 WO 2019105737A1 EP 2018081222 W EP2018081222 W EP 2018081222W WO 2019105737 A1 WO2019105737 A1 WO 2019105737A1
Authority
WO
WIPO (PCT)
Prior art keywords
machine learning
learning system
images
distance
determining
Prior art date
Application number
PCT/EP2018/081222
Other languages
German (de)
English (en)
Inventor
Johannes Maximilian DOELLINGER
Joerg Wagner
Original Assignee
Robert Bosch Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Priority to EP18807901.6A priority Critical patent/EP3718045A1/fr
Priority to CN201880076963.6A priority patent/CN111373411A/zh
Publication of WO2019105737A1 publication Critical patent/WO2019105737A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C3/00—Measuring distances in line of sight; Optical rangefinders
    • G01C3/02—Details
    • G01C3/06—Use of electric means to obtain final indication
    • G01C3/08—Use of electric radiation detectors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/026—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness by measuring distance between sensor and object
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/145—Illumination specially adapted for pattern recognition, e.g. using gratings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Definitions

  • the invention relates to a method for determining a distance to an object. Likewise, the invention relates to a computer program and apparatus each adapted to carry out the method.
  • DE 102011081384 B4 discloses a method for distance determination with the following steps: Determining a time offset between a change in a radiation characteristic of a headlamp of a vehicle and an effect of the change of the radiation pattern on a Schmbe range of an image. Determining a distance to an object imaged by the image area in the environment of the vehicle based on the time offset.
  • DE 102011005368 A1 discloses a driver assistance system for maneuvering and / or parking a vehicle with a video camera.
  • a video image is detected with an object located in the vicinity of the vehicle, and this video image is combined with other determined information (e.g.
  • the method having the features of independent claim 1 and the device have the following advantages: A more reliable determination of a distance to an object from a detected image is achieved, wherein the determination is not adversely affected by a reflection. Reflecting objects on smooth or mirrored surfaces can cause distance measurements based on images to produce incorrect results. The method and apparatus therefore provide a simple and cost effective way of making a more robust and reliable range finding that is unaffected by reflections or deceived. A further advantage is that the machine learning system does not require the development of complex image processing algorithms which firstly detect an object in an image, secondly determine their associated distance or associated depth information from the image and are thereby robust to reflections.
  • the machine learning system independently develops training data provided by a methodology to determine the distance to an object and to recognize reflections in the captured images and to take into account accordingly. As a result, during the determination of the distance, the method and the device are not deceived by reflections and therefore determine reliable distances.
  • the invention relates to a method for determining a
  • the method comprises the following steps:
  • a polarization filter arranged in front of a camera is used.
  • Determining the distance based on a second of the at least two captured images by means of a machine learning system.
  • the machine learning system also uses the first of the at least two captured images during the determination of the distance to disregard reflection in one of the at least two captured images when determining the distance.
  • determining a distance to an object it can be understood that a distance, in particular a distance, between a predefinable Reference point and the object is determined.
  • the reference point preferably corresponds to the position at which the camera is located.
  • the reference point can also be in front of or behind the camera. It should be noted that the method is independent of the position of the reference point, since depending on the selected reference point, the machine learning system can be adapted appropriately.
  • a reflection can be understood as any type of mirror image of an object that occurs. By way of example, by means of a reflecting surface, the mirror image of an object or a distortion and / or a rotation of the mirror image can occur, which are referred to below by the term reflection.
  • the at least two recorded images can either simultaneously or immediately after each other, in particular at a predeterminable time, who recorded the.
  • the images can also be captured by means of several differently positioned cameras.
  • the advantage according to this method is that the machine learning system is provided with sufficient information by the at least two captured images, which are differently filtered, that the reflection does not affect the distance determination.
  • one of the captured images is unfiltered and the other image is filtered with a polarizing filter or both have been detected filtered with a polarizing filter, wherein the polarizing filter each have a different orientation of the polarization planes.
  • the machine learning system is taught in such a way that the machine learning system determines the distance on the basis of the second of the at least two captured images. Furthermore, in this embodiment, the machine learning system can be taught in such a way that the machine learning system does not consider the reflection from the first of the at least two captured images when determining the distance.
  • the advantage of this method is that by learning the maschi nelle learning system, this independently learns a distance determination. Therefore, no complex algorithms have to be developed for this purpose in order to solve this complex image processing task. Furthermore, this is the learned machine Learning system in operation more computationally efficient or faster than conventional image processing algorithms for distance measurement based on captured images. For learning the machine learning system is similar to optimizing the academic learning system in all respects, and further, machine learning systems, especially neural networks, can achieve higher performance by concatenating the computing operations in the machine learning system.
  • the machine learning system is additionally trained to determine an object classification on the basis of the second of the at least two captured images and not to consider the reflection based on the first of the at least two captured images when determining the object classification. It is also advantageous if the machine learning system determines the object classification when determining the distance.
  • the maschi nelle learning system is additionally trained to an optical flow to ermit stuffs and is trained to reflect the reflection when determining the optical flow. Furthermore, the machine learning system determines an optical flow on the basis of the images acquired at the predeterminable successive time points.
  • a quantity in particular a vector, can be understood, which characterizes a movement of a point in the image, for example a speed and / or a direction of this point relative to a selected reference point. It is advantageous if the reference point of the optical flow and the reference point of the determination of the distance are at an identical position. It is advantageous if the captured images are stored and the machine learning system is learned by means of the stored captured images. This has the advantage that the method for determining the distance with the captured images is further improved in order to achieve a higher accuracy of the Ab measurement.
  • the actuator may comprise an at least partially autonomous machine, such as e.g. be a robot or a vehicle. It is also advantageous if the machine learning system is a deep neural network, in particular a "Revolutionary Neural Network” or a "Recurrent Neural Network”.
  • the invention in another aspect, relates to a device adapted to carry out the method according to the first aspect of the invention.
  • the device comprises the following features: At least one camera for capturing the at least two images and at least one polarization filter.
  • the polarization filter is arranged in front of the camera and is used for detecting the first of the at least two images.
  • the device also includes the machine learning system.
  • the device also the actuator, in particular a at least teilautonomome machine such. a robot or a vehicle.
  • the machine learning system is a deep neural network, in particular a "convolutional neural network” or a "recurrent neutral network”.
  • the advantage of the device is that by means of the particular linear polarization filter, reflections which are unpolarized are filtered out so that the captured image is an at least partially reflection-free image.
  • reflections which are unpolarized are filtered out so that the captured image is an at least partially reflection-free image.
  • the maschi nelle learning system can be carried out on this a precise distance determination, which is not affected by the reflection.
  • the polarizing filter comprises a plurality of consecutively arranged polarizing filter, wherein one of the polarization planes of the successively arranged polarizing filter is not perpendicular to the respective polarization planes of the successively arranged polarizing filter.
  • the advantage is that the polarization-filtered image can contain more reduced reflections.
  • the polarization filter is a circular polarization filter. This has the advantage that circular polarizing filters have higher compatibility with commonly used digital cameras, e.g. have an autofocus or an automated exposure meter.
  • the polarization filter additionally comprises a color filter.
  • the advantage here is that polarizing filters typically filter out the blue portion of the light, as it is highly unpolarized by the scattering of the light in the atmosphere. Therefore, the color filter can counteract the increased reduction of the blue component and preserve the color neutrality of the images.
  • the invention relates to a computer program which is set up to carry out one of the above-mentioned methods, that is to say comprises instructions which cause a computer, one of the above-mentioned
  • the invention relates to a machine-readable storage element on which the computer program is stored.
  • Fig. 1 is a schematic representation of a device for determining a
  • Fig. 2 is a schematic representation of an embodiment of a method for determining a distance to an object.
  • FIG. 1 shows a schematic representation of an exemplary device (10) for a reliable and robust determination of a distance to an object on the basis of a captured image.
  • the determination of the distance is not impaired by unwanted reflections, in particular by mirror images.
  • the device (10) comprises a machine learning system (11) which determines a distance to an object on the basis of at least two captured images (12a, 12b).
  • the machine learning system (11) is preferably a "convolutional neural network".
  • At least one of the at least two captured images (12b) is detected by a polarization filter (13) arranged in front of a camera.
  • the polarizing filter (13) By means of the polarizing filter (13), the unwanted reflections on smooth or reflective surfaces, such as on windows or on water surfaces can be suppressed.
  • the captured images (12a, 12b) are filtered differently.
  • the different filtered images (12a, 12b) are then used with the below-mentioned method of the machine learning system (11) to determine the distance.
  • the machine learning system (11) recognizes a reflection on the basis of the two differently filtered images and does not take these into account when determining the Distance.
  • the term "distance to an object” can be understood to mean that the machine learning system (11) determines a distance between a reference point and the object.
  • the reference point may be, for example, the position of the camera. Alternatively or additionally, a plurality of differently placed cameras can be used to capture the images. For this, the reference point must be chosen accordingly. It is also conceivable that the reference point lies in front of or behind or to the side of the camera, e.g. When the camera is placed on the windshield of a vehicle, the reference point near the bumper can be selected.
  • the machine learning system (11) can determine the distance to the object.
  • the machine learning system (11) uses another captured image (12b) to disregard reflection in the images while determining the distance. Because through the additional image (12b), the machine learning system (11) can be provided with further information so that the ma chine learning system (11) can detect reflection in the images (12a, 12b) used. This has the advantageous effect that the reflection is not included in the determination of the distance and thus erroneous result of the distance determination can be avoided the.
  • the result of the machine learning system (11) can optionally be used by a control unit of the device (10) to determine a control variable (14) depending on this result.
  • the control variable (14) can be used to control an actuator (17) who the.
  • the actuator (17) may be an at least partially autonomous machine, in particular a robot or a vehicle.
  • a parking operation of the at least partially autonomous machine with the control variable (14) can be carried out.
  • the actuator (17) can also readjust alignment of a polarization plane of the Polari sationsfilters (13) depending on the control variable (14), so that the result of the machine learning system (11) can be checked with another differently filtered image.
  • the machine learning system (11) may also generate a distance image.
  • the distance image can be an image in which the determined information of the machine Learning system (11), for example, the distance to the object, superimposed on a section of ei nes of the captured images (12a, 12b) are output. For example, each pixel can be assigned one of the determined information.
  • the machine learning system (11) obtains a plurality of captured images (12a, 12b), the images all being detected by means of different polarization filters. That one polarization plane of the polarization filters is oriented differently to the polarization planes of the other polarization filters so that differently filtered images can be captured and used for distance determination.
  • the device (10) comprises a computing unit (15) and a memory element (16) on which a computer program is stored.
  • the computer program may include instructions causing the computer program to run at e.g. the computing unit (15) is carried out one of the embodiments of the below-mentioned method.
  • Figure 2 shows a schematic representation of an embodiment of a method (20) for determining a distance to an object based on the captured images (12a, 12b).
  • the method (20) begins with step 21.
  • the machine learning system (11) of the device (10) is provided with a training data set, for example, from a database.
  • the training data set may include a plurality of training images, each of which may be real captured images or images generated by a computer.
  • the training images can be with or without reflections.
  • each of multiple images represent a same scene, but these are each Weil filtered differently, preferably with the polarizing filter (13) of Figure 1.
  • the training data are labeled by means of a distance value and be preferred with a note whether a reflection in the Training image is present or not.
  • the distance values of the training images can be determined by means of a "ground truth" method.
  • the labels can be used to train the machine learning system (11) in a more targeted way for distance determination, neglecting the reflections.
  • the machine learning system (11) is taught such that the machine learning system determines a distance to an object in the acquired image as a function of a captured image and taking into account a further acquired image.
  • the further acquired image is in particular a filtered image by means of a polarization filter or by means of a differently oriented polarization plane of the polarization filter.
  • the machine learning system can also be taught in such a way that the machine learning system, on the basis of the further provided image, does not consider reflections in the images for determining the distance to an object.
  • a gradient descent method is used for determining the parameter values of the machine learning system (11).
  • the gradient descent method can be applied to a cost function.
  • the cost function may be dependent on the parameters of the machine learning system (11) and preferably on the lab of the training data used.
  • the machine learning system (11) can also be taught in step 21 that the machine learning system (11) can determine an object detection, in particular an object classification, on the basis of the provided images.
  • the training images are preferably additionally or alternatively labeled, which characterize the object classes.
  • the machine learning system (11) can be taught in such a way that the machine
  • step 22 follows.
  • step 22 at least two images are acquired, in particular at a predefinable time.
  • a first of the at least two images is detected by means of a polarizing filter, which is arranged in front of the camera, filtered.
  • This polarization filter preferably has the same orientation from the plane of polarization as the polarization filters used for detecting the images of the training data set. This has the advantageous effect that the captured images are detected similarly to the training images of the machine learning system (11), whereby a higher accuracy of the determination of the distance to an object can be achieved.
  • the machine learning system (11) determines the distance to the object from one of the at least two captured images.
  • the further captured image in particular the image filtered by the polarization filter, is used during the determination of the distance to an object by the machine learning system that reflections in one of the two images are not taken into account for determining the distance to an object.
  • an object detection can also be determined in step 22 on the basis of the captured images (12a, 12b) become.
  • the advantageous effect here is that reflections in the images can be determined by the different filtered captured images and are not taken into account in the determination of the object detection by means of the machine learning system (11).
  • a reliable object detection can be achieved, because, for example, a reflection of aêt in a shop window can lead to erroneous object detection or classification.
  • step 23 follows optionally.
  • a control variable (14) for controlling the actuator (17) can be determined.
  • the actuator (17) in particular a machine that is at least partly autonomous, such as a robot or a vehicle, can, for example, execute a movement or driving maneuver depending on the control variable.
  • the machine learning system (11) can also be taught in step 21 such that the machine learning system (11) can determine an optical flow based on a sequence of captured images at different predefinable successive times .
  • the machine learning system (11) for this must be trained as well that reflections in the images are not taken into account for the determination of opti's flow.
  • the training images for learning the machine learning system (11) are additionally or alternatively labeled with labels that characterize the optical flow.
  • this can be learned with this Machine learning system (11) in step 22 of a plurality of different captured images, which were detected at predetermined times, the optical flow of an object determine.
  • step 21 can also be repeated several times in succession until a predeterminable sufficiently high accuracy of the distance measurement is achieved.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Electromagnetism (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Measurement Of Optical Distance (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention concerne un procédé (20) pour déterminer une distance à un objet. Ledit procédé comprend les étapes suivantes : la saisie d'au moins deux images (12a, 12b), un filtre de polarisation (13) placé devant une caméra étant employé pour saisir au moins une première image des deux images (12b) ou plus. La détermination de la distance à l'aide d'une seconde image de deux images (12b) ou plus saisies au moyen d'un système d'apprentissage automatique (11), durant la détermination de la distance, le système d'apprentissage automatique (11) employant également la première image des deux images (12a) ou plus afin de ne pas tenir compte d'une réflexion dans une image des deux images (12a, 12b) ou plus saisies lors de la détermination de la distance.La présente invention a trait en outre à un programme d'ordinateur et à un dispositif pour l'exécution du procédé (20) ainsi qu'à un élément de stockage lisible par machine (16) sur lequel est stocké le programme d'ordinateur.
PCT/EP2018/081222 2017-11-29 2018-11-14 Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet WO2019105737A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP18807901.6A EP3718045A1 (fr) 2017-11-29 2018-11-14 Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet
CN201880076963.6A CN111373411A (zh) 2017-11-29 2018-11-14 用于确定与对象的间距的方法、设备和计算机程序

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102017221381.7A DE102017221381A1 (de) 2017-11-29 2017-11-29 Verfahren, Vorrichtung und Computerprogramm zum Ermitteln eines Abstandes zu einem Objekt
DE102017221381.7 2017-11-29

Publications (1)

Publication Number Publication Date
WO2019105737A1 true WO2019105737A1 (fr) 2019-06-06

Family

ID=64456944

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2018/081222 WO2019105737A1 (fr) 2017-11-29 2018-11-14 Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet

Country Status (4)

Country Link
EP (1) EP3718045A1 (fr)
CN (1) CN111373411A (fr)
DE (1) DE102017221381A1 (fr)
WO (1) WO2019105737A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102021202454A1 (de) 2021-03-15 2022-09-15 Zf Friedrichshafen Ag Umgebungsmodellierung basierend auf Kameradaten
CN113256576B (zh) * 2021-05-18 2022-10-28 福州大学 基于偏振成像和机器学习的光学元件自动检测系统及方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010136344A1 (fr) * 2009-05-26 2010-12-02 Robert Bosch Gmbh Procédé d'acquisition d'images permettant l'acquisition de plusieurs images au moyen d'un système d'appareils de prise de vues pour automobiles et dispositif d'acquisition d'images associé du système d'appareils de prise de vues
DE102011005368A1 (de) 2011-03-10 2012-09-13 Robert Bosch Gmbh Fahrerassistenzsystem für ein Fahrzeug mit einem angereicherten Videobild in einem Anzeigemittel
DE102014115017A1 (de) * 2013-10-31 2015-04-30 Fuji Jukogyo Kabushiki Kaisha Fahrzeugsteuerungssystem
WO2017056821A1 (fr) * 2015-09-30 2017-04-06 ソニー株式会社 Dispositif d'acquisition d'informations et procédé d'acquisition d'informations
DE102011081384B4 (de) 2011-08-23 2017-05-04 Robert Bosch Gmbh Verfahren und Vorrichtung zur Abstandsbestimmung für ein Fahrzeug

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102004053416A1 (de) * 2004-11-05 2006-05-11 Robert Bosch Gmbh Konzeption einer Kamera mit Spiegeln zur Erzeugung eines Stereobildpaares
DE102011051583A1 (de) * 2011-07-05 2013-01-10 Conti Temic Microelectronic Gmbh Bildaufnahmevorrichtung für ein Fahrzeug
DE102012018121A1 (de) * 2012-09-13 2013-04-04 Daimler Ag Bilderfassungsvorrichtung
DE102014224762B4 (de) * 2014-12-03 2016-10-27 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zur Informationsgewinnung über ein Objekt in einem nicht einsehbaren, vorausliegenden Umfeldbereich eines Kraftfahrzeugs
WO2016130719A2 (fr) * 2015-02-10 2016-08-18 Amnon Shashua Carte éparse pour la navigation d'un véhicule autonome
DE102016206493A1 (de) * 2015-06-23 2016-12-29 Robert Bosch Gmbh Verfahren und Kamerasystem zur Entfernungsbestimmung von Objekten zu einem Fahrzeug
US10055652B2 (en) * 2016-03-21 2018-08-21 Ford Global Technologies, Llc Pedestrian detection and motion prediction with rear-facing camera

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010136344A1 (fr) * 2009-05-26 2010-12-02 Robert Bosch Gmbh Procédé d'acquisition d'images permettant l'acquisition de plusieurs images au moyen d'un système d'appareils de prise de vues pour automobiles et dispositif d'acquisition d'images associé du système d'appareils de prise de vues
DE102011005368A1 (de) 2011-03-10 2012-09-13 Robert Bosch Gmbh Fahrerassistenzsystem für ein Fahrzeug mit einem angereicherten Videobild in einem Anzeigemittel
DE102011081384B4 (de) 2011-08-23 2017-05-04 Robert Bosch Gmbh Verfahren und Vorrichtung zur Abstandsbestimmung für ein Fahrzeug
DE102014115017A1 (de) * 2013-10-31 2015-04-30 Fuji Jukogyo Kabushiki Kaisha Fahrzeugsteuerungssystem
WO2017056821A1 (fr) * 2015-09-30 2017-04-06 ソニー株式会社 Dispositif d'acquisition d'informations et procédé d'acquisition d'informations
US20180268246A1 (en) * 2015-09-30 2018-09-20 Sony Corporation Information acquisition apparatus and information acquisition method

Also Published As

Publication number Publication date
EP3718045A1 (fr) 2020-10-07
CN111373411A (zh) 2020-07-03
DE102017221381A1 (de) 2019-05-29

Similar Documents

Publication Publication Date Title
WO2015173092A1 (fr) Procédé et dispositif d'étalonnage d'un système de caméra de véhicule automobile
DE102009046295A1 (de) Optische Abweichungskorrektur für maschinelle Sichtinspektionssysteme
DE102006010735A1 (de) Fahrzeugumgebungsüberwachungsvorrichtung
EP2189349A2 (fr) Procédé et dispositif de compensation de l'angle de roulis
WO2019105737A1 (fr) Procédé, dispositif et programme d'ordinateur pour la détermination d'une distance à un objet
EP3938947A1 (fr) Procédé et dispositif pour la détermination d'une source de danger sur une voie de circulation
DE102012211961A1 (de) Verfahren und Vorrichtung zum Berechnen einer Veränderung eines Abbildungsmaßstabs eines Objekts
EP1098268A2 (fr) Méthode pour la mésure optique tridimensionelle de surfaces d'objets
DE102018103474A1 (de) Ein system und verfahren zur objektabstandserkennung und positionierung
EP3659113B1 (fr) Système de reconnaissance, procédé de travail et procédé d'apprentissage pour générer un modèle 3d avec des données de référence
DE102010021221A1 (de) Verfahren zur Bestimmung einer Ausrichtung einer an einem Fahrzeug angeordneten Kamera
DE102014009522A1 (de) Verfahren zur Erfassung einer Umgebung eines Fahrzeugs
EP4264565A1 (fr) Correction d'images à partir d'une caméra en cas de pluie, de lumière incidente et de contamination
WO2021073907A1 (fr) Étalonnage d'un système de capteur optique actif
DE102018207411A1 (de) Verfahren zur Ermittlung von Messinformationen in einem optischen Koordinatenmessgerät
DE102019007344A1 (de) Verfahren zur Ermittlung einer Schlupfbewegung eines Fahrzeugrades
WO2015149970A1 (fr) Procédé et dispositif d'adaptation d'une surface de projection tridimensionnelle pour la projection d'une pluralité d'images de caméra voisines
DE102020118723B4 (de) Ein Verfahren zum Bestimmen einer Position eines ersten Objekts in einem Positioniersystem, ein Positioniersystem und ein Computerprogramm
DE102009015500B4 (de) Verfahren und Vorrichtung zur Bestimmung einer Lageänderung eines Objekts mittels einer Stereokamera
DE102017217156B4 (de) Verfahren und Vorrichtung zur Ansteuerung eines Fahrerassistenzsystems unter Verwendung eines Stereokamerasystems mit einer ersten und einer zweiten Kamera
DE102007021107A1 (de) Vorrichtung zur Bestimmung eines Hauptpunktes eines Kamerasystems
DE102022116317A1 (de) Verfahren zur Detektion einer Verschmutzung auf einer Kamera eines Fahrzeugs, Kamera und Fahrerassistenzsystem
DE102016222548A1 (de) Verfahren und Steuergerät zur Bestimmung einer Verzeichnungseigenschaft eines optischen Mediums während des Fahrtbetriebs
DE102022207042A1 (de) Verfahren zur Erkennung einer Verdeckung eines Kamerasensors und Kamerasensor
DE102006013598A1 (de) Verfahren und Vorrichtung zur Korrespondenzbestimmung, vorzugsweise zur dreidimensionalen Rekonstruktion einer Szene

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18807901

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2018807901

Country of ref document: EP

Effective date: 20200629