System, method and computer program for generating a digital map of an environment

Abstract

The present disclosure relates to a system for generating a digital map of an environment. The system includes at least one sensor configured to record sensor data and a location of an object within the environment and a timestamp of recording the sensor data. Further, the system includes a data processing circuit configured to determine a time-dependent presence probability distribution of the object based on the sensor data. The presence probability distribution indicates the probability of the object being at its location before, after, and / or at a time before the timestamp. The data processing circuit is further configured to register the presence probability distribution of the object in a digital map of the environment of the object.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to a system for generating a digital map of an environment. In particular, embodiments relate to the concept of generating a digital map using an aircraft. Background Art

[0002] Digital maps play an important role in business and science in particular. For example, digital maps can be used for navigation purposes.

[0003] The established concepts provide static digital maps. In some applications, a time-dependent representation and / or a prediction of the future state of the environment may be required. For example, the time-dependent representation or prediction may also reflect (future) structural changes of the environment, such as structural changes of buildings or changes in the landscape. Further, they may allow time-dependent navigation.

[0004] Document US20190220989A1 describes a guidance system for a vehicle. The guidance system provides a distinction between stationary objects and dynamic objects. However, this concept does not provide a prediction of the future state of the environment.

[0005] Document US20130090787A1 discloses a three-dimensional map system that uses a radio altimeter, embedded GPS / INS and a map database for aircraft navigation. This concept can be used in particular to avoid collisions between aircraft and the ground. However, this concept does not provide a concept for generating time-dependent digital maps.

[0006] Therefore, improved concepts for digital maps may be needed.

[0007] This need is met by the subject matter of the accompanying independent and dependent claims. Summary of the invention

[0008] According to a first aspect, the present disclosure relates to a system for generating a digital map of an environment. The system comprises at least one sensor configured to record sensor data and positions of objects within the environment and a timestamp of recording the sensor data. Further, the system comprises a data processing circuit configured to determine a time-dependent presence probability distribution of the object based on the sensor data. The presence probability distribution indicates the probability that the object is at its position before, after and / or at the timestamp. The data processing circuit is further configured to register the presence probability distribution of the object in the digital map of the environment of the object.

[0009] An environment refers to, for example, an area or a space. Examples of environments include a public area, a landscape, or a traffic area.

[0010] Thus, an object may be a building, a natural structure (eg, a tree), a vehicle (eg, a car, truck, or motorcycle), or a person.

[0011] The sensor includes, for example, a camera, a three-dimensional (3D) imaging system (based on time of flight) (e.g., a stereo camera, an ultrasound system, a lidar system, or a radar system), or an occupancy sensor capable of detecting whether an object is within the sensed environment. The sensor may be fixedly mounted or may be movable. For the movable case, the sensor may be mounted to a mobile device, such as an unmanned aerial vehicle (UAV), also known as a "drone".

[0012] Thus, the sensor data may comprise (3D) image data or a three-dimensional point cloud representing the object.The sensor may comprise a clock for generating a timestamp indicating the time at which the sensor data was recorded.

[0013] In some embodiments, the system may include multiple sensors and / or combinations of the above sensors. This may enable the system to monitor the environment at multiple locations. Further, this may improve the reliability of the sensor data.

[0014] The data processing circuit may be a processor, a computer, a microcontroller, a field programmable array, a graphics processing unit (GPU), a central processing unit (CPU), or any programmable hardware.

[0015] If the sensor is mounted to the mobile device, the data processing circuit can be mounted remotely from the mobile device and the sensor or can be fixedly mounted. In the case of fixed mounting, the data processing circuit preferably transmits the sensor data via a wireless connection so as not to restrict the freedom of movement of the mobile device as a wired connection used to transmit the sensor data would.

[0016] The data processing circuitry can, for example, use object recognition to distinguish objects from a sensed background, as described in more detail later.

[0017] The time-dependent probability distribution can be understood as the time history of the probability that an object is at its (sensed) position in the environment. Specifically, the probability distribution includes the probability of sensing an object at another position in the environment before, when, and after the object is detected.

[0018] The probability distribution may, for example, have a maximum at the timestamp (detection time) and may decrease proportionally or exponentially over time and space from then on, and may depend on characteristics of the object indicating whether it is a stationary object or a moving object and how long the object remains within the environment.

[0019] In this way, the data processing circuitry may generate a time-dependent digital map of the environment. This may also be referred to as a "dynamic map". In some embodiments, the digital map may discard records of one or more sensed objects based on their probability distribution, for example, if the probability distribution drops below a predefined threshold after a period of time. Thus, the digital map may provide a time-dependent representation of the (contemporary) environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Some examples of the apparatus and / or method will be described below by way of example only and with reference to the accompanying drawings, in which

[0021] Figure 1 A system for generating a digital map of an environment is shown;

[0022] Figure 2 shows the time-dependent presence probability distribution of an object in an environment;

[0023] Figure 3 Several scenes of environmental observation are shown;

[0024] Figure 4 A flow chart schematically illustrating a method for generating a digital map of an environment is shown;

[0025] Figure 5a A recording environment is shown; and

[0026] Figure 5b The existence probability distribution is determined as shown. DETAILED DESCRIPTION

[0027] Various embodiments will now be described more fully with reference to the accompanying drawings, in which some examples are shown. In the accompanying drawings, the thickness of lines, layers and / or regions may be exaggerated for clarity.

[0028] Therefore, although other examples can have various modifications and alternative forms, some specific examples thereof are shown in the drawings and will be described in detail later. However, this detailed description does not limit other examples to the specific forms described. Other examples may encompass all modifications, equivalents, and substitutes that fall within the scope of the present disclosure. Throughout the description of the drawings, identical or similar numbers refer to identical or similar elements, which, when compared to each other, may be implemented identically or in modified form while providing identical or similar functions.

[0029] It should be understood that when an element is referred to as being "connected" or "coupled" to another element, the element may be directly connected or coupled via one or more intermediate elements. If "or" is used to combine two elements A and B, it should be understood to disclose all possible combinations, i.e., only A, only B, and A and B, if not explicitly or implicitly otherwise defined. Alternative wording for the same combination is "at least one of A and B" or "A and / or B". This also applies, mutatis mutandis, to combinations of more than two elements.

[0030] The terms used in this article to describe specific examples are not intended to limit other examples. Whenever a singular form such as "a", "an" and "the" is used and only a single element is neither explicitly nor implicitly defined as mandatory, other examples can also use multiple elements to implement the same function. Similarly, when a function is subsequently described as being implemented using multiple elements, other examples can use a single element or processing entity to implement the same function. It should be further understood that the terms "comprises", "comprising", "includes" and / or "including" specify the presence of the features, wholes, steps, operations, processes, actions, elements and / or parts when used, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, processes, actions, elements, parts and / or any groups thereof.

[0031] Unless otherwise defined, all terms (including technical and scientific terms) are used herein according to the common meaning in the art to which the examples belong.

[0032] In some applications, a dynamic / time-dependent digital map of an environment may be required. For example, a time-dependent digital map may also reflect structural changes in the environment, such as structural changes in buildings or changes in the landscape. Therefore, a time-dependent digital map is used, for example, to represent continuously changing areas.

[0033] The present disclosure relates to concepts for generating such time-dependent digital maps.

[0034] Figure 1 A system 100 for generating a time-dependent digital map 142 of an environment is shown.

[0035] The system 100 includes a sensor 110 for recording sensor data and a position of an object 130 and a timestamp for recording the sensor data.

[0036] The system 100 also includes, for example, a clock (not shown) for recording a timestamp indicating the time when the sensor 110 recorded the sensor data.

[0037] The sensor 110 comprises, for example, a camera. The camera 110 is, for example, an RGB / color-sensitive camera, a video camera, an infrared (IR) camera, or a combination thereof. Therefore, the sensor data may particularly comprise image data.

[0038] In alternative embodiments, the sensor 110 may include a lidar system, a radar system, an ultrasonic sensor, a time-of-flight camera, an occupancy sensor, or a combination thereof.

[0039] Under different weather conditions, each of the aforementioned embodiments of the sensor 110 may have a higher or lower resolution than other embodiments. Thus, the combination of multiple different sensors can improve the reliability of sensor data.

[0040] exist Figure 1 In the example of , the sensed environment corresponds to the field of view of camera 110 and includes object 130 .

[0041] Data processing circuitry 120 may determine a time-dependent presence probability distribution 122 that indicates the probability that object 130 is located at the sensed location before, after, and / or at the timestamp.

[0042] Figure 2 An example of generation of the presence probability distribution 122 is shown.

[0043] Data processing circuitry 120 may use object recognition to detect and characterize object 130 based on the image data.

[0044] The data processing circuit 120 can determine the position of the object 130 based on the geographic location of the camera 110 and the relative position of the object 130 with respect to the camera 110. To this end, the data processing circuit 120 determines the relative position based on the image data, and determines the geographic location of the camera 110 based on position data from a global positioning system (GPS) installed on the camera 110, for example.

[0045] The first diagram 190-1 shows detections 112 of the object 130 as probability peaks plotted over time and space. The detections 112 are mapped, for example, to the time of the timestamp and the location of the object in the first diagram 190-1.

[0046] The second diagram 190 - 2 shows an example of the existence probability distribution 122 .

[0047] To generate the presence probability distribution 122, the data processing circuit 120 may input the location and time stamp into a multi-dimensional function, and specifically a time- and space-dependent function. Figure 2 In the example of , the multidimensional function is, for example, a so-called “Gaussian kernel function.” Alternatively, the existence probability distribution 122 may correspond to an alternative (multidimensional) so-called “kernel function.”

[0048] Thus, there is a probability distribution 122 describing the probability that the object 130 is at any location within the environment at any point in time.

[0049] As can be seen from the second diagram 190 - 2 , the resulting presence probability distribution 122 has a maximum value, for example, at the time of the timestamp and the position of the object.

[0050] The object recognition may further provide a classification of the object 130 to adjust the parameters of the existence probability distribution / Gaussian kernel function 122 according to the classification of the object 130. These parameters specify, for example, the slope and / or the full width at half maximum of the Gaussian kernel function.

[0051] Object recognition may, for example, classify object 130 as a stationary object or a moving / active object. Parameters of the presence probability distribution 122 for stationary objects may be different from parameters of the presence probability distribution 122 for moving objects, such that the presence probability distribution 122 for stationary objects, for example, decreases more slowly than the presence probability distribution 122 for moving objects.

[0052] In addition, the data processing circuit 120 may register the existence probability distribution 122 of the object 130 in the digital map 142 of the environment. The digital map 142 is, for example, a spatial map representing the environment in a two-dimensional or three-dimensional space. Therefore, the data processing circuit 120 may register the existence probability distribution 122 according to the position of the object in the digital map 142.

[0053] The aforementioned system 100 can therefore provide a time-dependent digital map for a time-dependent representation of an environment. For example, this allows time-dependent navigation in some applications of the system 100.

[0054] System 100 may also detect object 130 multiple times.

[0055] For the (first) detection 112, the camera 110 may record first sensor data / first image data and a first position of the object and a first timestamp of the first sensor data / first image data at a first point in time, and for the second detection 112', the camera 110 may record second sensor data / second image data and a second position of the object and a second timestamp of the second sensor data / second image data at a second point in time. For the second detection 112', the data processing circuit 120 may apply object recognition to verify whether the object of the first detection and the object of the second detection are the same.

[0056] The third diagram 190 - 3 shows the first detection 112 and the second detection 112 ′ plotted over time and space.

[0057] As can be seen in the third diagram 190 - 3 , the second position of the object determined using the second detection 112 ′ may be different from the first position of the first detection 112 . This may be due to the motion of the object 130 .

[0058] The fourth diagram 190 - 4 shows the updated presence probability distribution 122 ′ resulting from the first detection 112 and the second detection 112 ′.

[0059] This concept may be applied analogously to further detections of object 130 using further sensor data sets / image data sets and corresponding time stamps.

[0060] The updated presence probability distribution 122' is, for example, a combination of the presence probability distribution 122 and another Gaussian kernel function that depends on the second timestamp of the second detection 112' and the second position of the object. Therefore, the data processing circuit 120 can update the digital map 142 with the updated presence probability distribution 122'. The updating of the presence probability distribution 122 thus enables the temporally and spatially dependent presence probability distribution of the object to be adjusted to obtain a more reliable and more accurate representation of the environment.

[0061] like Figure 1 As can be seen in FIG. 1 , the digital map 142 is for example stored on a (physical) data storage 140 connected to the data processing circuit 120. The data storage 140 may be a hard drive, an optical disk or the like.

[0062] The camera 110 may be mobile. This allows the sensed environment to be extended beyond the field of view of the camera 110. For example, the camera 110 may be integrated into a mobile device (eg, a vehicle, a handheld device, or a wearable device).

[0063] Figure 3 Several scenarios of using system 100 to observe an environment are shown.

[0064] In the illustrated scenario, the camera 110 is mounted to an unmanned aerial vehicle (UAV) 200. This enables the camera 110 to scan the environment at multiple locations from a bird's eye view. In this way, the camera 110 can detect multiple objects 130 located at multiple locations.

[0065] In the illustrated scene, the objects 130 correspond to one or more trees 130-1 (scene "1"), to a bridge 130-2 (scene "2"), to a building 130-3 (scene "3") and / or to a trailer 130-4 (scene "4"), with each object being located at one of a plurality of locations.

[0066] The camera 110 transmits sensor data (eg, image data) of the object 130 to the data processing circuit 120 , which in this case is a unified thread management (UTM) server, for example.

[0067] For example, as referenced Figure 1 and Figure 2 As depicted, server 120 generates and updates digital map 142 .

[0068] Due to the multiple detections, the system 100 can verify the classification of the tree 130-1, the bridge 130-2, and the building 130-3 as stationary objects and the classification of the trailer 130-4 as an active or moving object.

[0069] To avoid ambiguity errors, the server 120 may also be configured to recognize the objects 130 by their respective image data in subsequent detections. Thus, the server 120 may, for example, detect whether one of the objects 130 has been replaced by another object.

[0070] The server 120 may also be configured to determine the structure of the object 130 from the image data and each detection. The structure indicates, for example, the outline and / or appearance of the object 130.

[0071] In this way, if the structure of object 130 changes between multiple detections, server 120 can classify the object as a mutable / changing object. Tree 130-1, for example, can undergo seasonal changes. Therefore, server 120, for example, classifies tree 130-1 as a mutable / changing object.

[0072] In addition, server 120 may use object recognition to classify object 130 as "hollow" or "solid / full" by the structure of the object. For example, data processing circuit 130 may classify bridge 130-2 as a hollow object and building 130-3 as a solid object. This allows, for example, a more detailed representation of the environment.

[0073] The foregoing concepts can be further applied to applications that use multiple UAVs 200 to survey an environment and record sensor data. For example, those UAVs 200 can survey an environment at multiple locations simultaneously, which can speed up the recording of sensor data. This further enables the use of different UAVs 200 to detect objects 130 at different points in time.

[0074] Figure 4A flow chart schematically illustrating a method 400 for generating a digital map of an environment is shown. The method 400 comprises recording 410 sensor data and a position of an object within the environment and a timestamp of recording the sensor data. Furthermore, the method 400 comprises determining 420 a time-dependent presence probability distribution of the object based on the sensor data, wherein the presence probability distribution indicates a probability that the object is at the position before, after and / or at the timestamp. Furthermore, the method 400 registers 430 the presence probability distribution of the object in the digital map of the environment of the object.

[0075] Using the proposed system 100, the method 400 may allow generating a time-dependent digital map 142 of an environment. Thus, the time-dependent digital map may enable a time-dependent representation of the environment and / or a time-dependent navigation.

[0076] refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , further aspects and features of an implementation of method 400 are described in conjunction with system 100 .

[0077] Figure 5a and Figure 5b The recording 410 of sensor data and the determination 420 of the presence probability distribution 122 are shown in greater detail. Figure 5a and Figure 5b Especially involving the use Figure 3 Application of the method 400 of the UAV 200 .

[0078] As from Figure 5a It can be seen that the method 400 may also include transmitting 402 the predetermined flight trajectory from the server 120 to the UAV 200. To this end, the server 120 establishes a wireless connection to the UAV 200, for example.

[0079] The method 400 may further include checking 404 the availability and current accuracy of sensor data from the camera 110 , which may vary, for example, depending on surrounding weather conditions.

[0080] If the accuracy of the sensor data from the camera 110 is sufficient, the camera 110 surveys the environment along the flight trajectory and sends the sensor data to the server 120 .

[0081] If the accuracy of the sensor data from the camera 110 is not sufficient, the UAV 200 can check whether other sensors (such as lidar, radar, and / or ultrasonic sensors) are available 404 and whether the accuracy of the sensor data from the other sensors is sufficient. If the sensor data from the other sensors is sufficient, the UAV 200 can send those sensor data to the server 120.

[0082] In this way, UAV 200 is, for example, also able to survey the environment with sufficient accuracy in “bad weather conditions” (e.g., fog or rain), especially in cases where camera 110 is unable to provide sensor data with sufficient accuracy.

[0083] As described above, the method 400 includes recording 410 sensor data of the environment along the flight trajectory using the selected sensors. Additionally, the method 400 may include checking the sensor data for accuracy and transmitting the sensor data to the server 120.

[0084] Alternatively, if no available sensor 110 provides a predetermined sufficient accuracy, method 400 may retrieve 405 the UAV 200 to its base / home.

[0085] like Figure 5b As shown, method 400 transmits 406 the sensor data to server 120 and synchronizes 408 the sensor data with a digital map of the environment. To transmit 406 the sensor data, the sensor reestablishes a wireless connection to server 120, for example.

[0086] Subsequently, server 120 may continue to determine 430 a presence probability distribution 122 for sensed object 130 based on previous classifications of object 130 , as described above in conjunction with system 100 .

[0087] As described above, the server 120 categorizes the sensed objects 130 into, for example, “changing,” “immobile hollow,” and / or “immobile solid / full,” to determine their existence probability distribution 122 according to their categorization.

[0088] Thus, presence probability distribution 122 may be registered in digital map 142 of the environment, for example in the form of an additional (Gaussian) kernel function.

[0089] By adding a kernel function to the digital map, the digital map also becomes dynamic and reliable over time. Due to the use of various sensors, the system 100 can also survey the environment under "bad" weather conditions (e.g., rain, fog, snow), where visibility is lower than, for example, "good" weather conditions (e.g., sunshine).

[0090] Other embodiments relate to:

[0091] (1) A system for generating a digital map of an environment, comprising:

[0092] At least one sensor configured to

[0093] Recording sensor data and location of the object and a timestamp of when the sensor data was recorded; and

[0094] The data processing circuit is configured as follows:

[0095] determining a time-dependent presence probability distribution for the object based on the sensor data, wherein the presence probability distribution indicates a probability that the object is at its location before, after, and / or at the timestamp; and

[0096] The existence probability distribution of the object is registered in a digital map of the object's environment.

[0097] (2) A system according to (1), wherein the existence probability distribution comprises a time-dependent Gaussian kernel function that depends on the timestamp and the position of the object.

[0098] (3) A system according to any one of (1) to (2),

[0099] Wherein, the sensor is configured as:

[0100] Recording first sensor data and a first position of the object and recording a first timestamp of the first sensor data at a first point in time;

[0101] recording second sensor data and a second position of the object and recording a second timestamp of the second sensor data at a second point in time; and

[0102] Wherein, the data processing circuit is configured as follows:

[0103] A time-dependent presence probability distribution of the object is determined based on the first sensor data and the second sensor data, wherein the time-dependent presence probability indicates that the object is at a first position before, after and / or at a first time point and at a second position before, after and / or at a second time point.

[0104] (4) Based on the system of (3),

[0105] wherein the sensor is configured to record further sensor data sets and further positions of the object and to record respective timestamps of respective sensor data sets at further points in time;

[0106] Wherein, the data processing circuit is configured as follows:

[0107] A time-dependent presence probability distribution is determined based on the further sensor data set, the further position, the first time stamp, the second time stamp, and the further corresponding time stamps.

[0108] (5) The system according to (3) or (4), wherein the data processing circuit is configured to:

[0109] determining a structure of the object at a first point in time based on the first sensor data;

[0110] determining a structure of the object at a second point in time based on the second sensor data;

[0111] An object is classified as a mutable object if the structure of the object at a first point in time is different from the structure of the object at a second point in time.

[0112] (6) A system according to any one of (1) to (5), comprising:

[0113] a first sensor configured to record first sensor data and a first position of the object and a first timestamp for recording the first sensor data at a first point in time; and

[0114] The second sensor is configured to record second sensor data and a second position of the object and to record a second timestamp of the second sensor data at a second time point.

[0115] (7) The system of any of (1) to (6), wherein the data processing circuit is configured to classify the object as a moving object or a stationary object based on the sensor data of the object.

[0116] (8) The system according to any one of (1) to (7), wherein the sensor is mounted to the mobile device.

[0117] (9) The system of (8), wherein the mobile device is an unmanned aerial vehicle (UAV).

[0118] (10) A system according to any one of (1) to (9), wherein the sensor includes at least one of a lidar system, an ultrasonic system, a camera, a time-of-flight camera, and a radar system.

[0119] (11) A method for generating a digital map of an environment, comprising:

[0120] Recording sensor data and location of the object and a timestamp of when the sensor data was recorded; and

[0121] determining a time-dependent presence probability distribution of the object within the environment, the presence probability distribution depending on the timestamp and whether the object is within the environment; and

[0122] The existence probability distribution of objects is registered in a digital map of the environment.

[0123] (12) A computer program comprising instructions which, when executed by a processor, cause the processor to perform the method of (11).

[0124] with one or more of the previously detailed examples and appendices Figure 1 The aspects and features mentioned and described above may also be combined with one or more other examples, in order to replace similar features of other examples, or in order to additionally introduce features into other examples.

[0125] Examples may also be or relate to a computer program with a program code, which, when executed on a computer or processor, is used to perform one or more of the above methods. The steps, operations or processes of the above various methods may be formed by a programmed computer or processor. Examples may also encompass program storage devices, such as digital data storage media, which are machine, processor or computer readable and encode the machine executable, processor executable or computer executable program of the instructions. These instructions perform or cause some or all of the actions of the above methods to be performed. Program storage devices may include or be, for example, digital memories, magnetic storage media such as disks and tapes, hard drives or optically readable digital data storage media. Other examples may also cover computers, processors or control units programmed to perform the actions of the above methods, or (field) programmable logic arrays ((F)PLA) or (field) programmable gate arrays ((F)PGA) programmed to perform the actions of the above methods.

[0126] The description and the drawings only illustrate the principles of the present disclosure. In addition, all examples cited in this article are mainly and explicitly used for illustrative purposes only to help readers understand the principles of the present disclosure and the concepts contributed by the inventors to promote the development of the art. All statements citing the principles, aspects and examples of the present disclosure and their specific examples in this article are intended to cover their equivalents.

[0127] A functional block represented as a "means for..." performing a certain function may refer to a circuit configured to perform a certain function. Therefore, a "means for something" may be implemented as a "means configured or adapted for something", such as a device or circuit configured or adapted for a corresponding task.

[0128] The functions of the various elements shown in the drawings, including any functional blocks marked as "means", "means for providing a signal", "means for generating a signal", etc., can be implemented in the form of dedicated hardware such as "signal providers", "signal processing units", "processors", "controllers", etc., as well as hardware capable of executing software associated with appropriate software. When provided by a processor, these functions can be provided by a single dedicated processor, by a single shared processor, or by multiple separate processors, some or all of which can be shared. However, the term "processor" or "controller" is not limited to hardware specifically capable of executing software, but can include digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read-only memories (ROMs), random access memories (RAMs), and non-volatile memories for storing software. Other hardware, conventional and / or customized, may also be included.

[0129] For example, a block diagram may show a high-level circuit diagram that implements the principles of the present disclosure. Similarly, a flow chart, a flow diagram, a state transition diagram, a pseudo code, etc. may represent different processes, operations, or steps, which may be represented, for example, generally in a computer-readable medium and performed by a computer or processor, regardless of whether such a computer or processor is explicitly shown. The methods disclosed in the specification or claims may be implemented by an apparatus having means for performing each of the individual actions of the methods.

[0130] It should be understood that the disclosure of multiple actions, processes, operations, steps or functions disclosed in the specification or claims may not be interpreted as being in a specific order unless otherwise explicitly or implicitly stated, for example, for technical reasons. Therefore, unless these actions or functions are not interchangeable for technical reasons, the disclosure of multiple actions or functions will not limit these actions or functions to a specific order. In addition, in some examples, a single action, function, process, operation or step may include or may be divided into multiple sub-actions, sub-functions, sub-processes, sub-operations or sub-steps, respectively. Unless explicitly excluded, such sub-actions may be included and are part of the disclosure of the single action.

[0131] In addition, the following claims are hereby incorporated into the detailed description, where each claim may stand on its own as a separate example. Although each claim may stand on its own as a separate example, it should be noted that although a dependent claim may refer to a specific combination with one or more other claims in a claim, other examples may also include a combination of a dependent claim with the subject matter of each other dependent or independent claim. Such combinations are explicitly proposed herein unless a specific combination is stated to be absent. In addition, it is intended that features of a claim are also included in any other independent claim, even if that claim is not directly dependent on the independent claim.

Claims

1. A system for generating a digital map of an environment, comprising: At least one sensor configured to recording sensor data and positions of objects within the environment and a timestamp of recording the sensor data; as well as The data processing circuit is configured as follows: determining a time-dependent presence probability distribution of the object based on the sensor data, wherein the data processing circuit is configured to classify the object as a moving object or a stationary object based on the sensor data of the object, adjust parameters of the presence probability distribution according to the classification of the object, and input the location and the timestamp into a time- and space-dependent multidimensional function to generate the presence probability distribution, the presence probability distribution indicating the probability of the object being at the location before, after and / or at the timestamp; A presence probability distribution of the object is registered in the digital map of the object's environment.

2. The system according to claim 1, wherein: The presence probability distribution includes a time-dependent Gaussian kernel function that depends on the timestamp and the location of the object.

3. The system according to claim 1, in, The sensor is configured as: Recording first sensor data and a first position of the object and recording a first timestamp of the first sensor data at a first point in time; recording second sensor data and a second position of the object and recording a second timestamp of the second sensor data at a second point in time; and Wherein, the data processing circuit is configured as follows: A time-dependent existence probability distribution of the object is determined based on the first sensor data and the second sensor data, wherein the time-dependent existence probability represents the probability that the object is at the first position before, after and / or at the first time point and at the second position before, after and / or at the second time point.

4. The system according to claim 3, in, The sensor is configured to record further sensor data sets and further positions of the object and to record respective timestamps of respective sensor data sets at further points in time; Wherein, the data processing circuit is configured as follows: The time-dependent presence probability distribution is determined based on the further sensor data set, the further position, the first time stamp, the second time stamp, and the further corresponding time stamp.

5. The system according to claim 3, wherein: The data processing circuit is configured to: determining a structure of the object at the first point in time based on the first sensor data; determining a structure of the object at the second point in time based on the second sensor data; If the structure of the object at the first point in time is different from the structure of the object at the second point in time, the object is classified as a mutable object.

6. The system according to claim 4, comprising: a first sensor configured to record the first sensor data and a first position of the object and to record the first timestamp of the first sensor data at the first point in time; and A second sensor is configured to record the second sensor data and the second position of the object and record the second timestamp of the second sensor data at the second time point.

7. The system according to claim 1, wherein: The sensor is mounted to a mobile device.

8. The system according to claim 7, wherein: The mobile device is an unmanned aerial vehicle (UAV).

9. The system according to claim 1, wherein: The sensor includes at least one of a lidar system, an ultrasonic system, a camera, a time-of-flight camera, and a radar system.

10. A method for generating a digital map of an environment, comprising: recording sensor data and positions of objects within the environment and a timestamp of recording the sensor data; and determining a time-dependent presence probability distribution of the object based on the sensor data, wherein the object is classified as a moving object or a stationary object based on the sensor data of the object, a parameter of the presence probability distribution is adjusted according to the classification of the object, and the location and the timestamp are input into a time- and space-dependent multidimensional function to generate the presence probability distribution, the presence probability distribution indicating the probability of the object being at the location before, after and / or at the timestamp; and The presence probability distribution of the object is registered in a digital map of the object's environment.

11. A computer-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to perform the method according to claim 10.

Images