Positioning method, apparatus, and vehicle

Abstract

The present disclosure provides a positioning method, device and vehicle, the positioning method comprising: obtaining a target image; determining at least one map as a target map from a plurality of maps according to an environment scene, the environment scenes of different maps being different; in the case that the environment scene of the target image and the environment scene of the target map are both inconsistent, performing image enhancement processing on the target image and / or a map image of the target map to make the environment scene of the target image and the environment scene of the map image consistent; comparing the target image and the map image with consistent environment scenes; and determining a position corresponding to the target image according to a comparison result.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of computer vision, and in particular, to a positioning method, device and vehicle. BACKGROUND

[0002] The position of a device receiving a signal can be determined by a satellite positioning signal or other positioning signal. When the positioning signal is affected, such as signal interference, signal shielding, etc., positioning cannot be achieved. The positioning scheme using a visual map can achieve positioning in an environment where satellite positioning signals, Bluetooth signals, etc. are affected. Visual positioning applications can be widely used in positioning and navigation of robots, vehicles, etc. Due to the influence of various environmental factors such as light and climate, the images collected in different environmental scenes differ greatly, which may result in inaccurate positioning or positioning failure. SUMMARY

[0003] The present disclosure provides a positioning method, device and vehicle to at least solve the above technical problems in the prior art.

[0004] According to a first aspect of the present disclosure, a positioning method is provided, the method comprising:

[0005] obtaining a target image;

[0006] determining at least one map as a target map from a plurality of maps according to an environmental scene, the environmental scenes of different maps being different;

[0007] in a case where the environmental scene of the target image and the environmental scene of the target map are both inconsistent, performing image enhancement processing on the target image and / or a map image of the target map to make the environmental scene of the target image and the environmental scene of the map image consistent;

[0008] comparing the target image and the map image whose environmental scenes are consistent;

[0009] determining a position corresponding to the target image according to a comparison result.

[0010] In an implementable manner, the image enhancement processing on the target image and / or the map image of the target map comprises:

[0011] obtaining an environmental parameter of the target map; performing image enhancement processing on the target image to make the environmental parameter of the target image consistent with the environmental parameter of the target map; or

[0012] obtaining an environmental parameter of the target image; performing image enhancement processing on the map image of the target map to make the environmental parameter of the map image consistent with the environmental parameter of the target map.

[0013] In one embodiment, the target map includes a first target map and a second target map; image enhancement processing is performed on the target image and / or the map image of the target map, including:

[0014] Obtain first environmental parameters of the first target map and second environmental parameters of the second target map; perform first image enhancement processing on the target image based on the first environmental parameters to eliminate first environmental factors of the environmental scene in the target image; perform second image enhancement processing on the target image after the first image enhancement processing to make the environmental parameters of the second environmental factors of the environmental scene in the target image consistent with the second environmental parameters; or

[0015] Obtain the first environmental parameters of the first target map and the second environmental parameters of the second target map; perform a third image enhancement process on the target image to make the environmental parameters of the first environmental factor of the environmental scene of the target image consistent with the first environmental parameters; perform a fourth image enhancement process on the target image after the third image enhancement process to make the environmental parameters of the second environmental factor of the environmental scene of the target image consistent with the second environmental parameters.

[0016] In one possible implementation, determining the location corresponding to the target image based on the comparison results includes:

[0017] Identify a map image that matches the target image;

[0018] Determine the relative orientation and distance between the target image and the matching map image;

[0019] The location of the target image is determined based on the location corresponding to the map image and the relative orientation and distance.

[0020] In one possible implementation, comparing the target image with the map image, which has a consistent environmental scene, includes:

[0021] Extract the image features of the target image;

[0022] The image features of the target image are compared with the image features of the map image;

[0023] When the similarity between the image features of the target image and the image features of the map image reaches a threshold, the target image and the map image are matched.

[0024] The location corresponding to the target image is determined based on the location corresponding to the matching map image.

[0025] In one embodiment, the image features of the map image include a first image feature and a second image feature, wherein the second image feature is used to describe the environmental scene of the map image, and the first image feature is used for comparison and positioning of the target image and the map image.

[0026] In one possible implementation, before comparing the target image with the map image that matches the environmental scene, the method includes:

[0027] The target image is divided into multiple sub-images;

[0028] Delete the subgraph containing the dynamic obstacle;

[0029] Obtain the image features of the remaining sub-images;

[0030] The image features of the target image are obtained by merging the image features of each sub-image.

[0031] In one possible implementation, the multiple maps are obtained based on image enhancement processing of images.

[0032] In one possible implementation, the multiple maps are obtained based on image enhancement processing of images, including:

[0033] Obtain the original image of the target region;

[0034] Image enhancement processing is performed on the original image of the target area to obtain multiple target map images with different environmental scenes.

[0035] In one possible implementation, the method further includes:

[0036] Obtain the original image features, wherein the original image features are the image features of the original image;

[0037] Acquire each enhanced image feature, wherein the enhanced image features are the image features of the target map image for each environmental scene;

[0038] Extract the common part of the original image features and each of the enhanced image features corresponding to the same location as the first image features of the original image and each of the target map images;

[0039] The differences between the original image features and the enhanced image features corresponding to the same location are extracted as the second image features of the corresponding original image and the target map image.

[0040] In one possible implementation, acquiring the original image of the target region includes:

[0041] Collect video stream data of the target area and simultaneously collect geographic location data;

[0042] The video stream data is matched with the synchronously acquired geographic location data to obtain the original image of the target area corresponding to the geographic location data.

[0043] In one possible implementation, image enhancement processing is performed on the original image of the target area to obtain a target map image, including:

[0044] The original image is input into the trained model, and the trained model performs image enhancement processing on the original image to obtain the target map image.

[0045] In one possible implementation, training the model includes:

[0046] Acquire multiple images of the same location, each with a different environmental scene;

[0047] The model is trained by taking a first image from a plurality of images as input and a second image as output, wherein the first image and the second image have different environmental scenes.

[0048] According to a second aspect of this disclosure, a positioning device is provided, the device comprising:

[0049] The acquisition module is used to acquire images of the target object.

[0050] The determination module is used to select at least one map as the target map from multiple maps based on the environmental scenario. Different maps have different environmental scenarios.

[0051] An enhancement module is used to perform image enhancement processing on the target image and / or the map image of the target map when the environmental scene of the target image and the environmental scene of the target map are inconsistent, so as to make the environmental scene of the target image consistent with the environmental scene of the map image of the target map;

[0052] The positioning module is used to compare the target image with the map image of the map that matches the environmental scene, and determine the location corresponding to the target image based on the comparison result.

[0053] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0054] At least one processor; and

[0055] A memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0057] According to a fourth aspect of this disclosure, a vehicle is provided that includes the electronic equipment described in this disclosure.

[0058] According to a fifth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this disclosure.

[0059] In the positioning method disclosed herein, at least one map is selected from multiple maps as a target map for comparison with an acquired target image, based on the environmental scene. Different maps have different environmental scenes. If the environmental scene of the target image is inconsistent with that of the target map, image enhancement processing is performed on the target image and / or the map image of the target map to make the environmental scene of the target image consistent with that of the map image. The target image and the map image with consistent environmental scenes are then compared. Based on the comparison result, the location corresponding to the target image is determined. This embodiment of the disclosure increases positioning accuracy by using image enhancement to compare the target image and the map image of the target map under consistent environmental scenes.

[0060] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0061] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:

[0062] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0063] Figure 1 This illustration shows the implementation flow of the positioning method according to an embodiment of the present disclosure. Figure 1 ;

[0064] Figures 2a-2d A comparison diagram of the image enhancement process before and after is shown in the localization method of this embodiment of the present disclosure;

[0065] Figure 3 A schematic diagram of the implementation flow of the positioning method according to an embodiment of this disclosure is shown in Figure 2.

[0066] Figure 4 A schematic diagram of the composition structure of the positioning device according to an embodiment of the present disclosure is shown;

[0067] Figure 5 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0068] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0069] See Figure 1 This disclosure provides a positioning method, which includes:

[0070] Acquire the target image;

[0071] Based on the environmental scenario, at least one map is selected as the target map from multiple maps, and the environmental scenarios of different maps are different;

[0072] When the environmental scene of the target image is inconsistent with the environmental scene of the target map, image enhancement processing is performed on the target image and / or the map image of the target map to make the environmental scene of the target image consistent with the environmental scene of the map image.

[0073] Compare target images and map images that match the environmental scene;

[0074] Based on the comparison results, the location corresponding to the target image is determined.

[0075] In the positioning method of this disclosure, based on the environmental scene, at least one map is selected from multiple maps as a target map for comparison with the acquired target image. Different maps have different environmental scenes. If the environmental scenes of the target image and the target map are inconsistent, image enhancement processing is performed on the target image and / or the map image of the target map to make their environmental scenes consistent. The target image and the map image with consistent environmental scenes are then compared. Based on the comparison result, the location corresponding to the target image is determined. This embodiment of the disclosure increases positioning accuracy by using image enhancement to compare the target image and the map image of the target map under consistent environmental scenes.

[0076] The positioning method of this disclosure can be used for positioning and navigation of various robots, vehicles, etc., and can also be used in application scenarios such as store positioning. The method of acquiring the target image is not limited; it can be acquired in real time or a pre-acquired image can be used as the target image. The method of acquiring the target image can be selected according to the specific application scenario. For example, when the method of this disclosure is applied to the positioning or navigation of a vehicle or robot, an image of the surrounding environment can be acquired in real time using a camera on the vehicle or robot as the target image, thereby enabling real-time positioning and navigation of the vehicle or robot. Alternatively, when the method of this disclosure is applied to positioning a store in an image based on an existing image, the location of the store in the image can be determined, and the target image can be a pre-acquired image.

[0077] In this embodiment of the disclosure, the environmental scene can be divided according to one or more environmental factors such as different viewing angles, different lighting conditions, and different climates. For example, based on different lighting conditions, it can be divided into morning, daytime, evening, and night. The nighttime environmental scene can also be divided into several levels based on the brightness of the moon. Based on different climates, the environmental scene can be divided into different environmental scenes such as sunny, cloudy, rainy, snowy, and foggy. Specifically, each environmental scene can be further divided into several levels. Taking rain as an example, based on the amount of rainfall, the rainy environmental scene can be divided into 10 levels, from 1 to 10, with higher levels indicating heavier rainfall. Based on viewing angle, it can be divided into eye-level, upward-looking, downward-looking, and oblique-looking scenes. Similarly, the environmental scenes divided based on viewing angle can be further refined. For example, the oblique-looking environmental scene can be divided into left oblique-looking environmental scene, right oblique-looking environmental scene, etc., and downward-looking and upward-looking scenes can be divided into several levels according to the pitch angle, etc. Various environmental factors can also be combined to classify environmental scenarios. For example, based on climate and sunlight, it can be divided into rain during the day, rain in the evening, and rain at night. When combined with rainfall levels, environmental scenarios can be divided into level 1 rainfall during the day, level 3 rainfall during the day, etc.

[0078] In the method of this disclosure embodiment, at least one map can be determined as a target map from multiple maps based on the environmental scene. The target map is used to compare with a target image to achieve localization. When determining the target map, the environmental scene of the target image can be determined first, and each of the multiple maps corresponds to its own environmental scene. Based on the matching of the environmental scene of the target image and the environmental scene of the map, at least one map can be determined as the target map. If the environmental scene of the target image and the map are consistent based on the environmental scene matching, the target image can be compared with the map image of the target map to achieve localization. If the environmental scenes are not the same, image enhancement is used to make the environmental scene of the target image consistent with that of the target map.

[0079] In this embodiment, determining the corresponding target map based on the environmental scene of the target image can be achieved by matching the environmental scene of the target image with the environmental scene of the map. Based on the matching result, the map with the highest environmental scene similarity can be selected as the target map, or the top n maps sorted by environmental scene similarity can be selected as the target maps, where n is a positive integer. In specific implementations, the value of n can range from 1 to 5. The specific value of n can be determined based on at least one factor such as historical experience, computer resource consumption, and positioning accuracy. Alternatively, it can be determined as a percentage of the total number of maps. For example, if the total number of maps is 20, n = 20 * 15% = 3. If the total number of maps is 18, n = 18 * 15% = 2. Of course, when determining the specific value of n as a percentage of the total number of maps, if the calculation result includes a decimal part, it can be rounded, only the integer part can be taken, or one can be added to the integer part.

[0080] In other exemplary embodiments, when determining at least one map as the target map based on the matching result, a map whose environmental scene similarity reaches a preset first threshold can also be selected as the target map. For example, if the first threshold is 80%, then a map whose environmental scene similarity reaches 80% can be selected as the target map.

[0081] Of course, determining at least one map as the target map based on the matching results can also be based on two or more conditions. For example, let's take the top n maps ranked by environmental scene similarity and those reaching a first similarity threshold as the target maps. Here, n=2, and the first threshold is 75%. The number of maps with a similarity of 75% is 5. Based on the determination strategy, the top two maps ranked by environmental scene similarity are ultimately selected as the target maps and compared with the target image.

[0082] In practice, the target image's environmental scene is rainy at dusk, while the map's environmental scene includes both the evening and rainy environmental scenes. Based on the contrast of the environmental scenes, the evening and rainy environmental scenes are the most similar, so the maps of the evening and rainy environmental scenes are determined as the target maps.

[0083] The target map can be determined based on the environmental scene of the target image, or it can be determined in response to the user's selection. For example, the user selects a target map through input / output devices or touch operations, and the selected map is used as the target map for comparison with the target image to achieve positioning. Each map may include environmental scene information, and based on this information, the user can determine the environmental scene corresponding to each map. For example, at dusk, the user can select a map with an evening scene as the target map. When it's raining, the user can select a map with a rainy scene as the target map. The user can select through a menu, or the method disclosed herein can display a map display window on a display interface, showing selectable maps, and the user can select the corresponding map as the target map through clicks, lines, circles, or other operations.

[0084] Each map consists of multiple map images of the target area, and each map image corresponds to location information. By comparing the target image with the location information of the successfully matched map images, the location of the target image can be determined. The location information can be, for example, latitude and longitude coordinates.

[0085] The higher the consistency between the target image and the target map in terms of their environmental scenes, the easier it is to compare the target image and the target map, which can improve the accuracy of the positioning results.

[0086] In this embodiment, image enhancement processing is performed on the target image and / or the map image of the target map to ensure that the environmental scene of the target image and the map image are compared when the environmental scene is consistent. This improves the accuracy of positioning and reduces the probability of inaccurate positioning or positioning failure. While acquiring images in different environmental scenes and creating maps for different scenes can provide multiple environmental scene maps for positioning and navigation, acquiring images multiple times requires significant manpower and resources, and the acquisition time cannot be indefinite. Therefore, it is difficult to acquire enough maps of different environmental scenes, and during positioning or navigation, there is still a situation where the environmental scene of all maps is inconsistent with the environmental scene of the target image. This embodiment improves the problem of inaccurate positioning or positioning failure caused by inconsistencies between the environmental scenes of multiple maps and the target map by using image enhancement to ensure that the environmental scene of the target image and the map image are consistent.

[0087] In this embodiment of the disclosure, image enhancement processing can be performed on the target image, the map image of the target map, or both. In this embodiment, image enhancement can include spatial enhancement and pixel enhancement, etc. Specifically, further illustrative examples are provided in the embodiments described below.

[0088] In one embodiment, image enhancement processing is performed on the target image and / or the map image of the target map, including: obtaining environmental parameters of the target map; and performing image enhancement processing on the target image to make the environmental parameters of the target image consistent with the environmental parameters of the target map. In this embodiment, environmental parameters may include brightness, contrast, pixels, color levels, etc. Through image enhancement processing, the environmental parameters of the target image are adjusted to be consistent with the environmental parameters of the target map. The target map may correspond to environmental parameters, which are associated with the map during map creation. After determining the target map, the corresponding environmental parameters can be directly obtained. Of course, if the map is not associated with environmental parameters, the environmental parameters of the target map can also be obtained through calculation. For example, if the environmental scene of the target image is daytime and the environmental scene of the target map is nighttime with no lights, the environmental parameters associated with the target map are read, and the environmental parameters of the target image are adjusted to be consistent with the environmental parameters of the target map through image enhancement processing, so that the target image can be compared with the target map under the condition of consistent environmental scene.

[0089] In one embodiment, image enhancement processing is performed on the target image and / or the map image of the target map, including: acquiring environmental parameters of the target image; and performing image enhancement processing on the map image of the target map to make the environmental parameters of the map image consistent with the environmental parameters of the target map. In conjunction with the previous embodiment, the positioning method of this disclosure can also involve image enhancement processing on the map image of the target map. The specific values ​​of the environmental parameters of the target image can be calculated. Through image enhancement processing, the environmental parameters of the target image are adjusted to be consistent with the environmental parameters of the target map. In this embodiment, when performing image enhancement processing on the map image of the target map to adjust the environmental scene, all map images of the target map can be uniformly enhanced in the same way to generate a new map, facilitating the comparison between the map image and the target image, and enriching the maps for different environmental scenes. For example, if the environmental scene of the target image is daytime and the environmental scene of the target map is raining, the environmental parameters of the target image are calculated, and the environmental parameters of the map image of the target map are adjusted to be consistent with the environmental parameters of the target image through image enhancement processing, so that the environmental scene of the map image of the target map is adjusted to be daytime, thereby achieving comparison between the target image and the target map under the condition of consistent environmental scenes.

[0090] In one embodiment of this disclosure, the number of target maps can be two or more. For example, the target maps include a first target map and a second target map. Image enhancement processing of the target image and / or the map images of the target maps includes: obtaining a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a first image enhancement process on the target image based on the first environmental parameter to eliminate a first environmental factor of the target image's environmental scene; and performing a second image enhancement process on the target image after the first image enhancement process to make the environmental parameter of the second environmental factor of the target image's environmental scene consistent with the second environmental parameter. In this embodiment of the disclosure, two or more maps can be determined as target maps for comparison with the target image. Specifically, based on the environmental scene, the two maps whose environmental scene is closest to that of the target image can be selected as target maps. For ease of explanation, one is referred to as the first target map and the other as the second target map. For example, if the target image's environment is nighttime and rainy, its first environmental factor is illumination, and its second environmental factor is weather. The first target map's environment is also nighttime, with its first environmental parameter, while the second target map's environment is rainy, with its second environmental parameter. Through first image enhancement processing, the first environmental parameter is used to inversely eliminate the nighttime environmental factor in the target image, retaining the rainy environmental factor. Then, second image enhancement processing is performed on the target image with the retained rainy environmental factor, making the rainfall amount the same in both the target image and the second target map, allowing for comparison under identical environmental conditions. Alternatively, after inversely eliminating the first environmental factor in the target image, image enhancement processing can also be performed on the second target map to make the environmental parameters of both the target image and the second target map consistent.

[0091] In one possible implementation, the target map includes a first target map and a second target map. Image enhancement processing is performed on the target image and / or the map image of the target map, including: acquiring a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a third image enhancement processing on the target image to make the environmental parameters of a first environmental factor of the target image's environmental scene consistent with the first environmental parameter; and performing a fourth image enhancement processing on the target image after the third image enhancement processing to make the environmental parameters of the second environmental factor of the target image's environmental scene consistent with the second environmental parameter. Referring to the previous embodiment, the environmental scene of the target image is rainy at night, the environmental scene of the first target map is night, and the environmental scene of the second target map is rainy. Through the third image enhancement processing, the nighttime lighting conditions of the target image are made consistent with the nighttime lighting conditions of the first target map; through the fourth image enhancement processing, the rainfall in the target image is made consistent with the rainfall in the second target map. In this embodiment, the first target map and the second target map can be combined to obtain a third target map. The environmental parameters of the third target map are determined based on the first and second environmental parameters. The environmental scene of the third target map is rainy at night. Comparing the target image with the map image of the third target map allows for comparison when the environmental scenes are consistent.

[0092] In one embodiment, the target map includes a first target map and a second target map. Image enhancement processing is performed on the target image and / or the map images of the target map, including: acquiring first environmental parameters of the first target map and second environmental parameters of the second target map; performing first image enhancement processing on the target image to make the environmental parameters of the target image consistent with the environmental parameters of the first target map; and performing second image enhancement processing on the target image to make the environmental parameters of the target image consistent with the environmental parameters of the second target map. During comparison, after the first image enhancement processing, the target image and the first target map are first compared under the condition that the environmental scene is consistent. Then, after the second image enhancement processing, the target image and the second target map are compared again under the condition that the environmental scene is consistent. If both the first and second target maps at the same location successfully match the target image, then the corresponding position of the target image is determined based on that position. If one of them fails to match, the image enhancement processing is repeated, or the target map is re-determined. For example, the environmental scene of the target image is rain, the environmental scene of the first target map is level 2 rainfall, and the environmental scene of the second target map is level 3 rainfall. The rainfall in the target image is between level 2 and level 3. Image enhancement processing can be used to first adjust the rainfall in the target image to match the rainfall in the first target map, and then adjust the rainfall in the target image to match the rainfall in the second target map.

[0093] In this embodiment of the disclosure, the environmental scene of the target image and the target map image are consistent. This consistency can be determined when the similarity between the environmental scene of the target image and the environmental scene of the target map image reaches a second threshold. The second threshold can be greater than the first threshold. For example, the second threshold can be 95%, 96%, 98%, 99%, etc.

[0094] In this embodiment of the disclosure, when selecting a map based on the environmental scene, maps with a similarity reaching a second threshold can be selected first. If no map has a similarity reaching the second threshold, a target map is selected, and then image enhancement processing is performed. If a map has a similarity reaching the second threshold, a direct comparison is performed. When the number of maps with a similarity reaching the second threshold is greater than 1, the map with the highest similarity can be selected for comparison with the target image.

[0095] In one possible implementation, the target map includes a first target map and a second target map. Image enhancement processing is performed on the target image and / or the map image of the target map, including: acquiring environmental parameters of the target image; performing a first image enhancement process on the first target map to make the environmental parameters of the target image consistent with the environmental parameters of the first target map; and performing a second image enhancement process on the second target map to make the environmental parameters of the target image consistent with the environmental parameters of the second target map. The comparison is also performed separately, as detailed in the above embodiments.

[0096] In one possible implementation, determining the location corresponding to the target image based on the comparison results includes: determining a map image that matches the target image; determining the relative orientation and distance between the target image and the matching map image; and determining the location corresponding to the target image based on the location corresponding to the map image and the relative orientation and distance. By comparing, it can be determined whether the target image and the map image match, and the location corresponding to the target image can be determined based on the location corresponding to the matching map image. For example, based on the viewpoint of the target image, the size of objects in the image, and other information, it can be determined whether the location corresponding to the target image is closer to the photographer or farther away from the photographer relative to the location corresponding to the map image. In scenarios involving vehicle or robot positioning and navigation, the photographer is a vehicle or robot. Assuming the relative orientation of the target image relative to the map image is closer to the photographer, and the distance between the two is 5 meters, then the location corresponding to the target image can be determined based on the location corresponding to the matching map image.

[0097] In one embodiment, comparing a target image with a map image that matches the environmental scene includes: extracting image features from the target image; comparing the image features of the target image with the image features of the map image; when the similarity between the image features of the target image and the image features of the map image reaches a threshold, the target image and the map image are considered a match; and determining the location corresponding to the target image based on the location corresponding to the matched map image. In this embodiment, comparing the target image and the map image can determine whether they match based on their respective image features. The image features of the map image can be associated with a corresponding map image when the map is created. When comparing the target image and the map image, if the similarity between the image features of the target image and the image features of the map image reaches a threshold, it can be determined that the target image matches the map image. This threshold can be determined empirically, and it can also be adjusted in real time based on the positioning results. The threshold can be, for example, 90%, 95%, 98%, etc.

[0098] Both the image features of the target image and the image features of the map image can be extracted using models. Models for extracting image features include, but are not limited to: SIFT-based models (Scale-invariant feature transform), SURF-based or ORB-based bag-of-words models, or HASH-based image fingerprint models.

[0099] In one embodiment, the image features of the map image include first image features and second image features. The second image features describe the environmental scene of the map image, and the first image features can be used for comparison and localization between the target image and the map image. When determining the target map based on the environmental scene, the image features of the target image can be matched with the second image features. The target map can be determined based on the matching degree of the second image features. For details, please refer to the relevant descriptions of embodiments involving the determination of the target map. When comparing the image features of the target image with the image features of the map image to determine whether the target image and the map image match, the second image feature can be compared alone with the image features of the target image, or the first and second image features can be compared as a whole with the image features of the target image.

[0100] In one embodiment, before comparing the target image with a map image that matches the environmental scene, the localization method of this disclosure further includes: preprocessing the target image. Preprocessing can accurately extract image features from the target image. Preprocessing may include, but is not limited to, image enhancement, image filtering, image segmentation, image stretching, edge detection, and dynamic obstacle removal. In an exemplary embodiment, preprocessing the target image includes: dividing the target image into multiple sub-images; deleting the sub-image containing the dynamic obstacle; obtaining the image features of the remaining sub-images; and merging the image features of the sub-images to obtain the image features of the target image. Deleting the sub-image containing the dynamic obstacle can be determined based on the percentage of the dynamic obstacle's area in the sub-image. For example, when the percentage of the dynamic obstacle's area in the sub-image reaches a threshold, the sub-image is deleted. Specific threshold settings can be 5%, 10%, 20%, 30%, 50%, 70%, 80%, etc. Alternatively, all sub-images containing the dynamic obstacle can be deleted.

[0101] In one possible implementation, multiple maps are obtained based on image enhancement processing of images. By performing image enhancement processing on images, images of different environmental scenes are obtained, thereby establishing maps of different environmental scenes. This can improve the problems of high manpower, material resources, and time consumption in acquiring images of multiple environmental scenes, as well as incomplete environmental scene coverage.

[0102] In one implementation, multiple maps are obtained by image enhancement processing, including: acquiring an original image of a target area; and performing image enhancement processing on the original image of the target area to obtain multiple target map images with different environmental scenes. The image before image enhancement processing can be called the original image, and the image obtained by enhancing the original image can be called the target map image. The original image can be a captured image; for example, when building a map of a target area, an image acquisition device can be used to capture an image of the target area as the original image for image enhancement to obtain map images of different environmental scenes. The original image can also be an image obtained through image enhancement processing. For example, a map generated from an image of the target area acquired through acquisition can correspond to a first environmental scene. This image can be used as the original image for image enhancement processing, which can be called the first image enhancement processing, resulting in a map image called the first map image, and the corresponding environmental scene is called the second environmental scene. A first map can be built based on the first map image. When further expanding the map of different environmental scenes of the target area, the acquired image can continue to be used as the original image for image enhancement processing, or an image obtained through image enhancement processing, such as the first map image, can be used as the original image for image enhancement processing. See also Figures 2a-2d , Figure 2a The captured image corresponds to a daytime environment. Figure 2b , Figure 2c andFigure 2d These are images obtained through image enhancement processing for rainy days, nighttime without lights, and nighttime with lights, respectively.

[0103] In one possible implementation, see Figure 3 The positioning method of this disclosure further includes: acquiring original image features, where the original image features are the image features of the original image; acquiring enhanced image features, where the enhanced image features are the image features of the target map images of each environmental scene; extracting the common part of the original image features and the enhanced image features corresponding to the same position as the first image features of the original image and each target map image; and extracting the difference part of the original image features and the enhanced image features corresponding to the same position as the second image features of the corresponding original image and each target map image. The original image and the corresponding target map image obtained through image enhancement processing have the same position. The image features of the original image and the corresponding target map image obtained through image enhancement processing corresponding to the same position are merged, and the common part is extracted as the first image feature. The difference part between each image feature and other image features is extracted as the second image feature. The second image feature is used to describe the environmental scene. When extracting the common part and the difference part, thresholds can be set respectively. When the commonality of the image features reaches the corresponding threshold, it is used as the common part; when the difference of the image features reaches the corresponding threshold, it is used as the difference part. In specific implementations, features can be clustered, and the common part and the difference part can be extracted by setting a distance. For example, see... Figure 3 There are i images corresponding to the latitude and longitude coordinates (x, y), including the original image and the image obtained after image enhancement processing, numbered from 1 as image 1, image 2, ..., image i. The image features of images 1 to i are merged. The h image features of image 1 include feature 1-1, feature 1-2, ..., feature 1-h. After extraction, a features are taken as the common part and b features are taken as the difference part, where a+b≤h.

[0104] In one possible implementation, acquiring the original image of the target area includes: acquiring video stream data of the target area and simultaneously acquiring geographic location data; matching the video stream data with the simultaneously acquired geographic location data to obtain the original image of the target area corresponding to the geographic location data. While acquiring the video stream data, the geographic location data is acquired simultaneously. The geographic location data can be obtained through satellite positioning systems such as GPS and BeiDou. Because the video stream data and geographic location data are acquired synchronously, a correspondence between the video stream data and the geographic location data can be achieved based on time matching. For the same time point, the original image and the corresponding geographic location can be extracted, making the map image that constitutes the map correspond one-to-one with the geographic location. For example, for the synchronously acquired video stream data and geographic location data, at the time point of 20:30:40 on April 22, 2022, the latitude and longitude corresponding to the geographic location data is (x1, y1), and the original image extracted from the video stream is IM1. The correspondence between (x1, y1) and IM1 can be obtained. Image enhancement processing based on IM1 can obtain multiple target map images of environmental scenes, named IM2, IM3, ..., IMn respectively. IM1 to IMn all correspond to latitude and longitude (x1, y1). Image features of IM1 to IMn are extracted respectively, merged and processed, and the common parts and their respective differences are extracted to form image features corresponding to IM1 to IMn respectively.

[0105] In one implementation, image enhancement processing is performed on the original image of the target area to obtain a target map image. This includes: inputting the original image into a trained model, and having the trained model perform image enhancement processing on the original image to obtain the target map image. Using a trained model for image enhancement processing can improve the map creation effect and cover more environmental scenes. In specific implementations, the model used for image enhancement processing can be an AugGAN model or a SurfelGAN model, etc.

[0106] In one implementation, training the model includes: acquiring multiple images of the same location, each with a different environmental scene; training the model using a first image as input and a second image as output, where the environmental scenes of the first and second images are different. By using images of the same location with different environmental scenes as the input and output of the model respectively, a module for obtaining different environmental scenes can be obtained. Adversarial training can be used during model training. For example, using a first image of a daytime environmental scene as input and second images of environmental scenes with rainfall levels of 1-10 as output, the trained model can perform image enhancement processing on the original input image based on the environmental parameters corresponding to the rainfall levels of 1-10, thus obtaining images of the corresponding rainfall levels. The trained model can be used to build maps or for image enhancement processing to ensure that the environmental scenes of a target image and a target map image are consistent when comparing them.

[0107] See Figure 4 This disclosure provides a positioning device, which includes an acquisition module, a determination module, an enhancement module, and a positioning module. The acquisition module is used to acquire an image of a target object. The determination module is used to determine at least one map as the target map from multiple maps based on the environmental scene, wherein the environmental scenes of the different maps are different. The enhancement module is used to perform image enhancement processing on the target image and / or the map image of the target map when the environmental scenes of the target image and the target map are inconsistent, so that the environmental scenes of the target image and the map image of the target map are consistent. The positioning module is used to compare the target image with the map image of the map that has consistent environmental scene, and determine the position corresponding to the target image based on the comparison result.

[0108] In one embodiment, the enhancement module performs image enhancement processing on the target image and / or the map image of the target map, including: acquiring environmental parameters of the target map; and performing image enhancement processing on the target image to make the environmental parameters of the target image consistent with the environmental parameters of the target map.

[0109] In one embodiment, the enhancement module performs image enhancement processing on the target image and / or the map image of the target map, including: acquiring environmental parameters of the target image; and performing image enhancement processing on the map image of the target map to make the environmental parameters of the map image consistent with the environmental parameters of the target map.

[0110] In one embodiment, the target map includes a first target map and a second target map; the enhancement module performs image enhancement processing on the target image and / or the map image of the target map, including: obtaining a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a first image enhancement processing on the target image according to the first environmental parameter to eliminate a first environmental factor of the target image's environmental scene; and performing a second image enhancement processing on the target image after the first image enhancement processing to make the environmental parameter of the second environmental factor of the target image's environmental scene consistent with the second environmental parameter.

[0111] In one embodiment, the target map includes a first target map and a second target map; the enhancement module performs image enhancement processing on the target image and / or the map image of the target map, including: acquiring a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a third image enhancement processing on the target image to make the environmental parameter of the first environmental factor of the target image's environmental scene consistent with the first environmental parameter; and performing a fourth image enhancement processing on the target image after the third image enhancement processing to make the environmental parameter of the second environmental factor of the target image's environmental scene consistent with the second environmental parameter.

[0112] In one possible implementation, the positioning module determines the location corresponding to the target image based on the comparison results, including: determining a map image that matches the target image; determining the relative orientation and distance between the target image and the matching map image; and determining the location corresponding to the target image based on the location corresponding to the map image and the relative orientation and distance.

[0113] In one embodiment, the positioning module compares a target image with a map image that matches the environmental scene, including: extracting image features of the target image; comparing the image features of the target image with the image features of the map image; when the similarity between the image features of the target image and the image features of the map image reaches a threshold, the target image and the map image are matched; and determining the location corresponding to the target image based on the location corresponding to the matched map image.

[0114] In one embodiment, the image features of the map image include a first image feature and a second image feature. The second image feature is used to describe the environmental scene of the map image, and the first image feature is used for comparison and positioning of the target image and the map image.

[0115] In one embodiment, the device further includes a preprocessing module, which is used to: divide the target image into multiple sub-images; delete the sub-image containing the dynamic obstacle; obtain the image features of the remaining sub-images; and merge the image features of the sub-images to obtain the image features of the target image.

[0116] In one possible implementation, multiple maps are obtained based on image enhancement processing of images.

[0117] In one possible implementation, multiple maps are obtained based on image enhancement processing of images, including: acquiring the original image of the target area; performing image enhancement processing on the original image of the target area to obtain multiple target map images with different environmental scenes.

[0118] In one embodiment, the device further includes an extraction module, which is used to: acquire original image features, wherein the original image features are the image features of the original image; acquire each enhanced image feature, wherein the enhanced image features are the image features of the target map image of each environmental scene; extract the common part of the original image features and each enhanced image feature corresponding to the same position as the first image feature of the original image and each target map image; and extract the difference part of the original image features and each enhanced image feature corresponding to the same position as the second image feature of the corresponding original image and each target map image.

[0119] In one embodiment, the extraction module acquires the original image of the target area, including: acquiring video stream data of the target area and simultaneously acquiring geographic location data; matching the video stream data with the simultaneously acquired geographic location data to obtain the original image of the target area corresponding to the geographic location data.

[0120] In one embodiment, the enhancement module performs image enhancement processing on the original image of the target area to obtain a target map image, including: inputting the original image into a trained model, and the trained model performing image enhancement processing on the original image to obtain the target map image.

[0121] In one embodiment, training the model includes: acquiring multiple images at the same location, each image having a different environmental scene; training the model with a first image as input and a second image as output, wherein the first image and the second image have different environmental scenes.

[0122] The positioning device of this disclosure can implement the methods of the above embodiments, and the descriptions of the above method embodiments can be used to understand and explain the device of this disclosure. For the purpose of brevity and saving space, it will not be described again here.

[0123] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium. The electronic device of this disclosure embodiment is capable of performing the methods described herein.

[0124] According to embodiments of this disclosure, a vehicle is also provided that includes the electronic devices described above. The electronic devices include an in-vehicle terminal and a portable, stand-alone electronic device.

[0125] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0126] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0127] like Figure 5 As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0128] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the positioning method. For example, in some embodiments, the positioning method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the positioning method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the positioning method by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0135] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0136] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0138] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A positioning method, characterized in that, The method includes: Acquire the target image; Based on the environmental scenario, at least one map is selected as the target map from multiple maps, and the environmental scenarios of different maps are different; When the environmental scene of the target image and the environmental scene of the target map are inconsistent, image enhancement processing is performed on the target image and / or the map image of the target map to make the environmental scene of the target image consistent with the environmental scene of the map image; the target map includes a first target map and a second target map; the image enhancement processing on the target image and / or the map image of the target map includes: obtaining a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a first image enhancement processing on the target image according to the first environmental parameter to eliminate a first environmental factor of the environmental scene of the target image; performing a second image enhancement processing on the target image after the first image enhancement processing to make the environmental parameter of the second environmental factor of the environmental scene of the target image consistent with the second environmental parameter; or obtaining the first environmental parameter of the first target map and the second environmental parameter of the second target map; performing a third image enhancement processing on the target image to make the environmental parameter of the first environmental factor of the environmental scene of the target image consistent with the first environmental parameter; performing a fourth image enhancement processing on the target image after the third image enhancement processing to make the environmental parameter of the second environmental factor of the environmental scene of the target image consistent with the second environmental parameter; The target image and the map image, which match the environmental scene, are compared; Based on the comparison results, the location corresponding to the target image is determined.

2. The method according to claim 1, characterized in that, Image enhancement processing is performed on the target image and / or the map image of the target map, including: Obtain the environmental parameters of the target map; perform image enhancement processing on the target image to make the environmental parameters of the target image consistent with the environmental parameters of the target map; or Obtain the environmental parameters of the target image; perform image enhancement processing on the map image of the target map to make the environmental parameters of the map image consistent with the environmental parameters of the target map.

3. The method according to claim 1, characterized in that, Based on the comparison results, the location corresponding to the target image is determined, including: Identify a map image that matches the target image; Determine the relative orientation and distance between the target image and the matching map image; The location of the target image is determined based on the location corresponding to the map image and the relative orientation and distance.

4. The method according to claim 1, characterized in that, Comparing the target image with the map image, which matches the environmental scene, includes: Extract the image features of the target image; The image features of the target image are compared with the image features of the map image; When the similarity between the image features of the target image and the image features of the map image reaches a threshold, the target image and the map image are matched. The location corresponding to the target image is determined based on the location corresponding to the matching map image.

5. The method according to claim 4, characterized in that, The image features of the map image include a first image feature and a second image feature. The second image feature is used to describe the environmental scene of the map image, and the first image feature is used for comparison and positioning of the target image and the map image.

6. The method according to claim 1, characterized in that, Before comparing the target image with the map image that matches the environmental scene, the method includes: The target image is divided into multiple sub-images; Delete the subgraph containing the dynamic obstacle; Obtain the image features of the remaining sub-images; The image features of the target image are obtained by merging the image features of each sub-image.

7. The method according to claim 1, characterized in that, The multiple maps are obtained by performing image enhancement processing on the images.

8. The method according to claim 7, characterized in that, The multiple maps are obtained based on image enhancement processing of images, including: Obtain the original image of the target region; Image enhancement processing is performed on the original image of the target area to obtain multiple target map images with different environmental scenes.

9. The method according to claim 8, characterized in that, The method further includes: Obtain the original image features, wherein the original image features are the image features of the original image; Acquire each enhanced image feature, wherein the enhanced image features are the image features of the target map image for each environmental scene; Extract the common part of the original image features and each of the enhanced image features corresponding to the same location as the first image features of the original image and each of the target map images; The differences between the original image features and the enhanced image features corresponding to the same location are extracted as the second image features of the corresponding original image and the target map image.

10. The method according to claim 8, characterized in that, Obtain the raw image of the target region, including: Collect video stream data of the target area and simultaneously collect geographic location data; The video stream data is matched with the synchronously acquired geographic location data to obtain the original image of the target area corresponding to the geographic location data.

11. The method according to claim 8, characterized in that, The original image of the target area is subjected to image enhancement processing to obtain a target map image, including: The original image is input into the trained model, and the trained model performs image enhancement processing on the original image to obtain the target map image.

12. The method according to claim 11, characterized in that, Training the model includes: Acquire multiple images of the same location, each with a different environmental scene; The model is trained by taking a first image from a plurality of images as input and a second image as output, wherein the first image and the second image have different environmental scenes.

13. A positioning device, characterized in that, The device includes: The acquisition module is used to acquire the target image; The determination module is used to select at least one map as the target map from multiple maps based on the environmental scenario. Different maps have different environmental scenarios. An enhancement module is configured to perform image enhancement processing on the target image and / or the map image of the target map when the environmental scene of the target image and the environmental scene of the target map are inconsistent, so as to make the environmental scene of the target image consistent with the environmental scene of the map image of the target map; the target map includes a first target map and a second target map; performing image enhancement processing on the target image and / or the map image of the target map includes: obtaining a first environmental parameter of the first target map and a second environmental parameter of the second target map; performing a first image enhancement processing on the target image according to the first environmental parameter to eliminate a first environmental factor of the environmental scene of the target image; performing a second image enhancement processing on the target image after the first image enhancement processing to make the environmental parameter of the second environmental factor of the environmental scene of the target image consistent with the second environmental parameter; or obtaining the first environmental parameter of the first target map and the second environmental parameter of the second target map; performing a third image enhancement processing on the target image to make the environmental parameter of the first environmental factor of the environmental scene of the target image consistent with the first environmental parameter; and performing a fourth image enhancement processing on the target image after the third image enhancement processing to make the environmental parameter of the second environmental factor of the environmental scene of the target image consistent with the second environmental parameter. The positioning module is used to compare the target image with the map image of the map that matches the environmental scene, and determine the location corresponding to the target image based on the comparison result.

14. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.

15. A vehicle, characterized in that, Includes the electronic device as described in claim 14.

Images