WO2022078440A1 - 一种包含运动物体的空间占用率采集判断设备及方法 - Google Patents

一种包含运动物体的空间占用率采集判断设备及方法 Download PDF

Info

Publication number
WO2022078440A1
WO2022078440A1 PCT/CN2021/123789 CN2021123789W WO2022078440A1 WO 2022078440 A1 WO2022078440 A1 WO 2022078440A1 CN 2021123789 W CN2021123789 W CN 2021123789W WO 2022078440 A1 WO2022078440 A1 WO 2022078440A1
Authority
WO
WIPO (PCT)
Prior art keywords
space
image acquisition
image
acquisition device
model
Prior art date
Application number
PCT/CN2021/123789
Other languages
English (en)
French (fr)
Inventor
左忠斌
左达宇
Original Assignee
左忠斌
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 左忠斌 filed Critical 左忠斌
Publication of WO2022078440A1 publication Critical patent/WO2022078440A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • the invention relates to the technical field of topography measurement, in particular to the technical field of 3D topography measurement.
  • space occupancy assessment needs to be performed in many scenarios, such as assessing the amount of traffic flow on the road, that is, assessing the occupancy status of vehicles on the road; whether the liquid in the pipeline has filled the entire pipeline, etc.
  • Such requirements are currently realized by means of special sensors (such as flow), but devices made in this way are only suitable for one scenario (for example, flow sensors can only be used to measure liquid flow).
  • the commonly used methods include the use of machine vision and structured light, laser ranging, and lidar.
  • Structured light, laser ranging, and lidar all require an active light source to be emitted to the target, which will affect the target in some cases, and the cost of the light source is high.
  • the structure of the light source is relatively precise and easy to be damaged.
  • the machine vision method is to collect pictures of objects from different angles, and match and stitch these pictures to form a 3D model, which is low-cost and easy to use.
  • multiple cameras can be set at different angles of the object to be tested, or pictures can be collected from different angles by rotating a single or multiple cameras. But no matter which of the two methods, the acquisition position of the camera needs to be set around the target (referred to as the surround type), but this method requires a large space to set the acquisition position for the image acquisition device.
  • the present invention provides a device and method for judging the space occupancy situation that overcomes the above problems or at least partially solves the above problems.
  • Embodiments of the present invention provide a device and method for judging space occupancy, including a 3D information collection device and a first processor;
  • the space contains moving objects
  • the 3D information collection device is used to scan the space at different times to obtain multiple images that can synthesize the 3D model
  • the first processor is configured to compare the three-dimensional models obtained at different times, so as to determine the space occupied by the object.
  • the first processor is also used to synthesize the three-dimensional model.
  • a second processor is further included, and the 3D information acquisition device includes or is connected to the second processor for synthesizing the three-dimensional model.
  • the images collected by the 3D information collection device are a three-dimensional model capable of synthesizing space, and/or multiple images capable of synthesizing a three-dimensional model of space-objects.
  • the 3D information acquisition device includes an image acquisition device and a rotation device
  • the image acquisition device is connected with the rotating device, and the rotating device drives it to rotate;
  • the angle ⁇ between the optical axes of the image acquisition device at two adjacent acquisition positions satisfies the following conditions:
  • R is the distance from the rotation center to the surface of the target object
  • T is the sum of the object distance and the image distance during acquisition
  • d is the length or width of the photosensitive element of the image acquisition device
  • F is the lens focal length of the image acquisition device
  • u is the experience coefficient.
  • u ⁇ 0.498 for better synthesis effect, preferably u ⁇ 0.411, especially preferably u ⁇ 0.359, in some applications, u ⁇ 0.281, or u ⁇ 0.169, or u ⁇ 0.041, or u ⁇ 0.028.
  • the optical acquisition ports of the image acquisition device are all facing away from the direction of the rotation axis.
  • the comparison is a comparison of a three-dimensional space model with a three-dimensional space-object model; or a comparison of a three-dimensional space-object model at different times.
  • the processor outputs the matching result to the display device, the printing device, and/or the action execution device.
  • the different moments are preset time scales.
  • the current space occupancy rate can be obtained efficiently and accurately.
  • FIG. 1 shows a schematic structural diagram of an implementation manner of a 3D information collection device provided by an embodiment of the present invention
  • FIG. 2 shows a schematic structural diagram of another implementation manner of the apparatus for collecting 3D information provided by an embodiment of the present invention.
  • FIG. 3 shows a schematic structural diagram of a third implementation manner of a 3D information collection apparatus provided by an embodiment of the present invention.
  • FIG. 4 shows a schematic structural diagram of a fourth implementation manner of a 3D information collection apparatus provided by an embodiment of the present invention.
  • Fig. 5 shows a schematic diagram of collecting a spatial 3D model by a 3D information collecting device provided by an embodiment of the present invention.
  • FIG. 6 shows a schematic diagram of jointly performing 3D collection of a space and objects in a space by a 3D information collection apparatus provided by an embodiment of the present invention.
  • the present invention provides a 3D information acquisition device, as shown in FIG.
  • the image acquisition device 1 is connected with the rotating shaft of the rotating device 2 , and the rotating device 2 drives it to rotate.
  • the acquisition direction of the image acquisition device is a direction away from the rotation center. That is, the acquisition direction is directed outward relative to the center of rotation.
  • the optical axis of the image acquisition device may be parallel to the rotation plane, or may form a certain angle with the rotation plane, for example, within the range of -90°-90° based on the rotation plane.
  • the rotation axis or its extension line ie, the rotation center line
  • the acquisition method (surround type) in which the traditional image acquisition device rotates around a certain object, that is, it is completely different from the surround type in which the image acquisition device rotates around the target object.
  • the optical collection ports (eg lenses) of the image collection device are all facing away from the direction of the rotation axis, that is to say, the collection area of the image collection device has no intersection with the rotation center line.
  • the optical axis of the image acquisition device has an included angle with the horizontal plane, so this method is also quite different from the general autorotation method, especially the target object whose surface is not perpendicular to the horizontal plane can be acquired.
  • the rotating shaft of the rotating device can also be connected to the image capturing device through a deceleration device, for example, through a gear set or the like.
  • the image capturing device rotates 360° on the horizontal plane, it captures an image corresponding to the target at a specific position (the specific shooting position will be described in detail later). This shooting can be performed in synchronization with the rotation action, or after the shooting position stops rotating, and then continues to rotate after shooting, and so on.
  • the above-mentioned rotating device may be a motor, a motor, a stepping motor, a servo motor, a micro motor, or the like.
  • the rotating device (for example, various types of motors) can rotate at a specified speed under the control of the controller, and can rotate at a specified angle, so as to realize the optimization of the collection position.
  • the specific collection position will be described in detail below.
  • the rotating device in the existing equipment can also be used, and the image capturing device can be installed thereon.
  • the carrying device 3 is used to carry the weight of the entire equipment, and the rotating device 2 is connected with the carrying device 3 .
  • the carrying device may be a tripod, a base with a supporting device, or the like.
  • the rotating device is located in the center part of the carrier to ensure balance. However, in some special occasions, it can also be located at any position of the carrying device. Furthermore, the carrying device is not necessary.
  • the swivel device can be installed directly in the application, eg on the roof of a vehicle.
  • an image capturing device 1 a rotating device 2 , a carrying device 3 , and a telescopic device 4 are included.
  • the image acquisition device 1 is connected with the rotating shaft of the rotating device 2 , and the rotating device 2 drives it to rotate.
  • the rotating shaft of the rotating device can also be connected to the image capturing device through a deceleration device, for example, through a gear set or the like.
  • the image capturing device rotates 360° on the horizontal plane, it captures an image corresponding to the target at a specific position (the specific shooting position will be described in detail later). This shooting can be performed in synchronization with the rotation action, or after the shooting position stops rotating, and then continues to rotate after shooting, and so on.
  • the above-mentioned rotating device may be a motor, a motor, a stepping motor, a servo motor, a micro motor, or the like.
  • the rotating device (for example, various types of motors) can rotate at a specified speed under the control of the controller, and can rotate at a specified angle, so as to realize the optimization of the collection position.
  • the specific collection position will be described in detail below.
  • the rotating device in the existing equipment can also be used, and the image capturing device can be installed thereon.
  • One end of the telescopic device 4 is connected to the rotating device 2, and the other end is connected to the bearing device 3, and is used to expand and contract in a direction perpendicular to the optical axis of the image capture device, so that the image capture device can be positioned at different positions. At each position, it is rotated and scanned by the rotating device, so that a 3D model of the target at that position can be constructed. After scanning a certain position, the telescopic device moves again, so that the image acquisition device moves to another position, repeating the above scanning, and so on, to realize the construction of the internal 3D model of the slender target. It can also be used to scan at different height levels when the surrounding target is high, so as to construct a 3D model of the entire target.
  • the telescopic device can be various telescopic structures such as telescopic sleeves and telescopic slide rails. Its telescoping can be adjusted manually or under the control of the control unit.
  • the telescopic device may also include a telescopic motor for driving the telescopic unit (eg, a telescopic sleeve) to extend or shorten. After telescopic in place, the length of the telescopic device can be locked by the locking unit to provide stable support for the rotating device.
  • the locking unit may be a mechanical locking unit, such as a locking pin, etc., or an electric locking unit, for example, under the control of the control unit, to lock the telescopic device.
  • the carrying device 3 is used to carry the weight of the entire device.
  • the carrying device may be a tripod, a base with a supporting device, or the like.
  • the rotating device is located in the center part of the carrier to ensure balance. However, in some special occasions, it can also be located at any position of the carrying device. Furthermore, the carrying device is not necessary.
  • the rotating device can be installed directly in the application equipment, for example, it can be installed on the top of the walking robot.
  • the image acquisition device can collect information at different heights, so that for buildings with high indoor ceilings, comprehensive and accurate acquisition can be achieved.
  • the 3D information acquisition device includes an image acquisition device 1 , a rotation device 2 , a carrying device 3 , and a pitch device 5 .
  • the image acquisition device 1 is arranged on the tilt device 5, so that the image acquisition device 1 can tilt and rotate along the vertical plane.
  • the pitching device can be rollers, gears, bearings, ball joints, etc.
  • the optical axis of the image acquisition device is usually parallel to the pitch direction, but it can also form a certain angle in some special cases.
  • the pitching device can be adjusted manually, or it can be pitched and rotated under the driving of the motor, so as to realize the precise pitch angle adjustment according to the program control.
  • the tilting device further includes a locking mechanism for locking the tilting device after the tilting angle is adjusted in place and the optical axis of the image capturing device is at a predetermined angle with the horizontal plane, thereby preventing it from rotating in the vertical direction again.
  • the pitching device 5 is connected with the rotating shaft of the rotating device 2 , and is driven by the rotating device 2 to rotate.
  • the rotating shaft of the rotating device can also be connected to the pitching device through a reduction gear, for example, through a gear set or the like.
  • the optical axis of the image acquisition device Due to the adjustment of the tilting device, the optical axis of the image acquisition device usually forms a certain angle with the horizontal plane. This allows scanning of targets whose surfaces are not perpendicular to the horizontal. That is, according to the approximate angle between the surface of the target object and the horizontal plane, the tilting device is adjusted so that the optical axis of the image acquisition device is perpendicular to the surface of the target object as much as possible, so as to improve the acquisition accuracy of the details of the target object. Of course, it can also be parallel to the horizontal plane in special cases.
  • the image acquisition device can be properly tilted upward, thereby making the acquisition range larger.
  • a telescopic device 4 and a pitching device 5 may be included at the same time. That is, the image capturing device 1 is installed on the pitching device 5 , the pitching device 5 is connected to the rotating device 2 , the rotating device 2 is installed on one end of the telescopic device 4 , and the other end of the telescopic device 4 is installed on the carrying device 3 .
  • the image acquisition device can be positioned at different heights in turn through the telescopic rod, and then scanned and acquired in sequence, or the pitch angle can be adjusted to make the image acquisition device Collect more upper space information.
  • both can be used at the same time depending on the situation.
  • the acquisition direction of the image acquisition device is the direction away from the rotation center. That is, the acquisition direction is directed outward relative to the center of rotation.
  • the optical axis of the image acquisition device may be parallel to the rotation plane, or may form a certain angle with the rotation plane, for example, within the range of -90°-90° based on the rotation plane.
  • the rotation axis or its extension line ie, the rotation center line
  • passes through the image acquisition device that is, the image acquisition device still rotates in an autorotation manner. This is essentially different from the acquisition method (surround type) in which the traditional image acquisition device rotates around a certain object, that is, it is completely different from the surround type in which the image acquisition device rotates around the target object.
  • optical collection ports (eg lenses) of the image collection device are all facing away from the direction of the rotation axis, that is to say, the collection area of the image collection device has no intersection with the rotation center line.
  • this method is also quite different from the general self-rotation method, especially the target object whose surface is not perpendicular to the horizontal plane can be collected.
  • the above device may further include a ranging device, the ranging device is fixedly connected with the image acquisition device, and the pointing direction of the ranging device is the same as the direction of the optical axis of the image acquisition device.
  • the distance measuring device can also be fixedly connected to the rotating device, as long as it can rotate synchronously with the image capturing device.
  • an installation platform may be provided, the image acquisition device and the distance measuring device are both located on the platform, the platform is installed on the rotating shaft of the rotating device, and is driven and rotated by the rotating device.
  • the distance measuring device can use a variety of methods such as a laser distance meter, an ultrasonic distance meter, an electromagnetic wave distance meter, etc., or a traditional mechanical measuring tool distance measuring device.
  • the 3D acquisition device is located at a specific location, and its distance from the target has been calibrated, and no additional measurement is required.
  • the light source can also include a light source, and the light source can be arranged on the periphery of the image acquisition device, on the rotating device and on the installation platform.
  • the light source can also be set independently, for example, an independent light source is used to illuminate the target. Even when lighting conditions are good, no light source is used.
  • the light source can be an LED light source or an intelligent light source, that is, the parameters of the light source are automatically adjusted according to the conditions of the target object and the ambient light.
  • the light sources are distributed around the lens of the image capture device, for example, the light sources are ring-shaped LED lights around the lens. Because in some applications it is necessary to control the intensity of the light source.
  • a diffuser device such as a diffuser housing
  • a diffuser housing can be arranged on the light path of the light source.
  • directly use the LED surface light source not only the light is softer, but also the light is more uniform.
  • an OLED light source can be used, which has a smaller volume, softer light, and has flexible properties, which can be attached to a curved surface.
  • marking points can be set at the position of the target. And the coordinates of these markers are known. By collecting marker points and combining their coordinates, the absolute size of the 3D composite model is obtained. These marking points can be pre-set points or laser light spots.
  • the method for determining the coordinates of these points may include: 1Using laser ranging: using a calibration device to emit laser light toward the target to form a plurality of calibration point spots, and obtain the calibration point coordinates through the known positional relationship of the laser ranging unit in the calibration device. Use the calibration device to emit laser light toward the target, so that the light beam emitted by the laser ranging unit in the calibration device falls on the target to form a light spot.
  • the laser beams emitted by the laser ranging units are parallel to each other, and the positional relationship between the units is known. Then the two-dimensional coordinates on the emission plane of the multiple light spots formed on the target can be obtained.
  • the distance between each laser ranging unit and the corresponding light spot can be obtained, that is, depth information equivalent to multiple light spots formed on the target can be obtained. That is, the depth coordinates perpendicular to the emission plane can be obtained.
  • the three-dimensional coordinates of each spot can be obtained.
  • 2 using the combination of distance measurement and angle measurement: respectively measure the distance of multiple markers and the angle between each other, so as to calculate the respective coordinates.
  • Use other coordinate measurement tools such as RTK, global coordinate positioning system, star-sensing positioning system, position and pose sensors, etc.
  • the position of the 3D acquisition device 6 is set so that its field of view scanning range covers the space, and mainly covers the space. For example, when monitoring the traffic flow of a certain road, the road surface and the area with a certain height above it can be regarded as the space. Instead, each car traveling on the road is considered to be a corresponding object.
  • the acquisition device is installed high and the space to be acquired is located below, the image acquisition device of the equipment needs to be turned to a certain depression angle before acquisition.
  • the rotating device drives the image acquisition device to rotate at a certain speed, and the image acquisition device performs image acquisition at a set position during the rotation process. At this time, the rotation may not be stopped, that is, the image acquisition and the rotation are performed synchronously; or the rotation may be stopped at the position to be acquired, image acquisition is performed, and the rotation continues to the next position to be acquired after the acquisition is completed.
  • the rotating device can be driven by a pre-programmed control unit program. It can also communicate with the upper computer through the communication interface, and control the rotation through the upper computer. In particular, it can also be wired or wirelessly connected to the mobile terminal, and the rotation of the rotating device can be controlled by the mobile terminal (eg, a mobile phone). That is, the rotation parameters of the rotating device can be set through the remote platform, cloud platform, server, host computer, and mobile terminal to control the start and stop of its rotation.
  • the image acquisition device collects multiple images of the target, and sends the images to the remote platform, cloud platform, server, host computer and/or mobile terminal through the communication device, and uses the 3D model synthesis method to perform 3D synthesis inside the target space.
  • the length of the telescopic device is controlled so that the image acquisition device is located at a predetermined position, the rotating device drives the image acquisition device to rotate at a certain speed, and the image acquisition device performs image acquisition at the set position during the rotation.
  • the rotation may not be stopped, that is, the image acquisition and the rotation are performed synchronously; or the rotation may be stopped at the position to be acquired, image acquisition is performed, and the rotation continues to the next position to be acquired after the acquisition is completed.
  • the rotating device can be driven by a pre-programmed control unit program. It can also communicate with the upper computer through the communication interface, and control the rotation through the upper computer.
  • the rotation of the rotating device can also be wired or wirelessly connected to the mobile terminal, and the rotation of the rotating device can be controlled by the mobile terminal (eg, a mobile phone). That is, the rotation parameters of the rotating device can be set through the remote platform, cloud platform, server, host computer, and mobile terminal to control the start and stop of its rotation.
  • the mobile terminal eg, a mobile phone
  • the length of the telescopic device is controlled so that the image acquisition device is located at another predetermined position, and the above-mentioned action of the rotating device is repeated, so that the image acquisition device can acquire the image of the target object surrounding the position, and so on, and the acquisition is performed at multiple height positions to obtain images, thereby Build the corresponding 3D model.
  • the image acquisition device collects multiple images of the target, and sends the images to the remote platform, cloud platform, server, host computer and/or mobile terminal through the communication device, and uses the 3D model synthesis method to perform 3D synthesis of the target.
  • the tilting device can also be controlled so that the image acquisition device is tilted to a certain angle, and then rotated and acquired.
  • the acquisition device is usually installed on it, and the image acquisition device needs to rotate a certain depression angle before scanning.
  • the distance measuring device can be used to measure the corresponding distance parameters in the relevant formula conditions, that is, the distance from the rotation center to the target, and the distance from the sensing element to the target, before or at the same time as the acquisition.
  • the collection position is calculated according to the corresponding conditional formula, and the user is prompted to set the rotation parameters, or the rotation parameters are automatically set.
  • the rotating device can drive the distance measuring device to rotate, so as to measure the above two distances at different positions.
  • the two distances measured at multiple measurement points are averaged respectively, and are brought into the formula as the unified distance value collected this time.
  • the average value may be obtained by a summation average method, a weighted average method, or another average value method, or a method of discarding abnormal values and averaging again.
  • the space-object 3D model When there is an object to be monitored in the space, a 3D model common to the object and the space can be obtained at this time, which is hereinafter referred to as the space-object 3D model.
  • the specific method is the same as the above, except that the target has changed from a single space to a space and objects accommodated in the space.
  • the device since the object is moving, the device needs to collect at different times, so as to obtain 3D models of the space and the object at multiple times.
  • the road surface and the area with a certain height on it can be regarded as the space
  • each car driving on the road can be regarded as a corresponding object
  • the collection device can scan the road space and the surface at the same time.
  • Image of a moving vehicle When the acquisition device is installed high and the space to be acquired is located below, the image acquisition device of the equipment needs to be turned to a certain depression angle before acquisition.
  • the acquisition device collects images of multiple space-objects, stamps each image with a time stamp, and then performs 3D synthesis, thereby obtaining 3D models of multiple space-objects.
  • the specific collection method is the same as the above-mentioned spatial collection, and will not be repeated here.
  • the acquisition device collects images of multiple space-objects, stamps each image with a time stamp, and then performs 3D synthesis, thereby obtaining 3D models of multiple space-objects again.
  • the time interval between T0 and T1 may be shorter or longer as required.
  • the shortest interval is: the acquisition device keeps rotating, so that the acquisition is uninterrupted.
  • the 3D model of the space-object collected at the time Tn is used for three-dimensional comparison with the 3D model of the space collected and obtained, so as to obtain the degree of the space occupied by the object at this time. For example, a 3D model of an empty street is collected, and then a 3D model of a street with traffic flow is collected at a certain time, and the ratio of the street occupied by vehicles can be obtained by comparing the two.
  • the 3D model of the space-object acquired by the acquisition at time Tn and the 3D model of the space-object acquired by the acquisition at time Tm can also be used for three-dimensional comparison, so as to obtain the proportional change of the space occupied at two different times.
  • the above judgment process may be completed in the collection device, or may be completed in a server or a cloud platform. That is to say, any acquisition device can independently obtain the image of the target object, complete 3D synthesis in it, and compare and judge 3D models at different times, and send the judgment result to the server or user. However, it is also possible to complete image acquisition and 3D synthesis only in the acquisition device, and complete the judgment on a remote processor, server or cloud platform. In another case, each device can collect the image of the target independently, but transmit it to the remote processor, server or cloud platform through the communication network, synthesize the 3D model of the target on them, and complete the comparison of the 3D model judge. This can greatly reduce the cost of each collection device.
  • the method of optimizing the camera acquisition position can also be adopted.
  • the prior art for such a device does not mention how to better optimize the camera position.
  • some optimization methods exist they are obtained under different empirical conditions under different experiments.
  • some existing position optimization methods need to obtain the size of the target object, which is feasible in surround 3D acquisition and can be measured in advance.
  • the present invention conducts a large number of experiments, and summarizes the following empirical conditions that the interval of camera acquisition is preferably satisfied during acquisition.
  • the included angle ⁇ of the optical axis of the image acquisition device at two adjacent positions satisfies the following conditions:
  • R is the distance from the center of rotation to the surface of the target
  • T is the sum of the object distance and the image distance during acquisition, that is, the distance between the photosensitive unit of the image acquisition device and the target object.
  • d is the length or width of the photosensitive element (CCD) of the image acquisition device.
  • CCD photosensitive element
  • F is the focal length of the lens of the image acquisition device.
  • u is the empirical coefficient.
  • a distance measuring device such as a laser distance meter
  • a distance measuring device is configured on the acquisition device. Adjust its optical axis to be parallel to the optical axis of the image acquisition device, then it can measure the distance from the acquisition device to the surface of the target object. Using the measured distance, according to the known positional relationship between the distance measuring device and the various components of the acquisition device, you can Get R and T.
  • the distance from the photosensitive element to the surface of the target object along the optical axis is taken as T.
  • multiple averaging methods or other methods can also be used. The principle is that the value of T should not deviate from the distance between the image and the object during acquisition.
  • the distance from the center of rotation to the surface of the target object along the optical axis is taken as R.
  • multiple averaging methods or other methods can also be used, the principle of which is that the value of R should not deviate from the radius of rotation at the time of acquisition.
  • the size of the object is used as a method for estimating the position of the camera in the prior art. Because the size of the object will change with the change of the measured object. For example, after collecting 3D information of a large object, when collecting small objects, it is necessary to re-measure the size and re-calculate. The above-mentioned inconvenient measurements and multiple re-measurements will bring about measurement errors, resulting in incorrect camera position estimation.
  • the empirical conditions that the camera position needs to meet are given, and there is no need to directly measure the size of the object.
  • d and F are fixed parameters of the camera. When purchasing a camera and lens, the manufacturer will give the corresponding parameters without measurement.
  • R and T are only a straight line distance, which can be easily measured by traditional measurement methods, such as ruler and laser rangefinder.
  • the acquisition direction of the image acquisition device eg, camera
  • the orientation of the lens is substantially opposite to the rotation center.
  • u should be less than 0.498.
  • u ⁇ 0.411 is preferred, especially u ⁇ 0.359.
  • the multiple images acquired by the image acquisition device are sent to the processing unit, and the following algorithm is used to construct a 3D model.
  • the processing unit may be located in the acquisition device, or may be located remotely, such as a cloud platform, a server, a host computer, and the like.
  • the specific algorithm mainly includes the following steps:
  • Step 1 Perform image enhancement processing on all input photos.
  • the following filters are used to enhance the contrast of the original photo and suppress noise at the same time.
  • g(x, y) is the gray value of the original image at (x, y)
  • f(x, y) is the gray value of the original image after enhancement by Wallis filter
  • m g is the local gray value of the original image.
  • sg is the local grayscale standard deviation of the original image
  • mf is the local grayscale target value of the transformed image
  • sf is the localized grayscale standard deviation target value of the transformed image.
  • c ⁇ (0,1) is the expansion constant of the image variance
  • b ⁇ (0,1) is the image luminance coefficient constant.
  • the filter can greatly enhance the image texture patterns of different scales in the image, so it can improve the number and accuracy of feature points when extracting image point features, and improve the reliability and accuracy of matching results in photo feature matching.
  • Step 2 Extract feature points from all the input photos, and perform feature point matching to obtain sparse feature points.
  • the SURF operator is used to extract and match the feature points of the photo.
  • the SURF feature matching method mainly includes three processes, feature point detection, feature point description and feature point matching. This method uses Hessian matrix to detect feature points, uses Box Filters to replace second-order Gaussian filtering, uses integral image to accelerate convolution to improve calculation speed, and reduces the dimension of local image feature descriptors, to speed up matching.
  • the main steps include 1 constructing the Hessian matrix to generate all interest points for feature extraction.
  • the purpose of constructing the Hessian matrix is to generate image stable edge points (mutation points); 2 constructing the scale space feature point positioning, which will be processed by the Hessian matrix
  • Each pixel point is compared with 26 points in the two-dimensional image space and scale space neighborhood, and the key points are initially located.
  • (3) The main direction of the feature point is determined by using the harr wavelet feature in the circular neighborhood of the statistical feature point. That is, in the circular neighborhood of the feature points, the sum of the horizontal and vertical harr wavelet features of all points in the 60-degree sector is counted, and then the sector is rotated at intervals of 0.2 radians, and the harr wavelet eigenvalues in the region are counted again.
  • the direction of the sector with the largest value is used as the main direction of the feature point; (4) a 64-dimensional feature point description vector is generated, and a 4*4 rectangular area block is taken around the feature point, but the direction of the obtained rectangular area is along the main direction of the feature point. direction.
  • Each sub-region counts the haar wavelet features of 25 pixels in the horizontal and vertical directions, where the horizontal and vertical directions are relative to the main direction.
  • the haar wavelet features are 4 directions after the horizontal value, after the vertical value, after the absolute value of the horizontal direction and the sum of the absolute value of the vertical direction.
  • the matching degree is determined by calculating the Euclidean distance between the two feature points. The shorter the Euclidean distance, the better the matching degree of the two feature points. .
  • Step 3 Input the coordinates of the matched feature points, and use the beam method to adjust the position and attitude data of the sparse target object 3D point cloud and the camera to obtain the sparse target object model 3D point cloud and position model coordinates.
  • Sparse feature points Take sparse feature points as the initial value, perform dense matching of multi-view photos, and obtain dense point cloud data.
  • stereo pair selection For each image in the input dataset, we select a reference image to form a stereo pair for computing the depth map. So we can get a rough depth map for all images, these depth maps may contain noise and errors, and we use its neighborhood depth map to perform a consistency check to optimize the depth map for each image.
  • depth map fusion is performed to obtain a 3D point cloud of the entire scene.
  • Step 4 Use dense point cloud to reconstruct the target surface. It includes several processes of defining octrees, setting function spaces, creating vector fields, solving Poisson equations, and extracting isosurfaces.
  • the integral relationship between the sampling point and the indicator function is obtained from the gradient relationship
  • the vector field of the point cloud is obtained according to the integral relationship
  • the approximation of the gradient field of the indicator function is calculated to form the Poisson equation.
  • the approximate solution is obtained by matrix iteration
  • the isosurface is extracted by the moving cube algorithm
  • the model of the measured object is reconstructed from the measured point cloud.
  • Step 5 Fully automatic texture mapping of the target model. After the surface model is constructed, texture mapping is performed.
  • the main process includes: 1 texture data acquisition through image reconstruction of the target surface triangle mesh; 2 visibility analysis of the reconstructed model triangle. Use the calibration information of the image to calculate the visible image set of each triangular face and the optimal reference image; 3.
  • the triangular face is clustered to generate texture patches.
  • the triangular surface is clustered into several reference image texture patches; 4
  • the texture patches are automatically sorted to generate texture images. Sort the generated texture patches according to their size relationship, generate a texture image with the smallest enclosing area, and obtain the texture mapping coordinates of each triangular surface.
  • the method of the present invention can also be used to monitor the water flow of the canal.
  • it can be motion on a larger time scale, such as the deformation of a mountain.
  • each acquisition device obtains the 3D model separately and then imports it into the processor for judgment. It is also possible to directly import the pictures of each acquisition device into the processor, and centrally complete the synthesis of the 3D model in the processor, as well as the comparison and judgment between them. .
  • This can simplify the structure and cost of acquisition hardware. For example, it is not necessary to set a processor capable of processing large data in the acquisition device, and only simple control is required.
  • the collected images can be transmitted to the cloud platform (equivalent to a processor) for centralized processing through 4G, 5G or other communication networks. This is also one of the inventive points of the present invention.
  • the processor After the comparison and judgment is completed, the processor outputs the judgment result to the display device for display, or prompts the user, such as outputting to a mobile terminal interface such as a computer and a mobile phone; or outputting it to a printing device for 2D or 3D printing, which is convenient for on-site operation, viewing and use; It can also be directly connected with the action mechanism, for example, directly connected with the traffic lights to control the traffic lights of the street.
  • the above-mentioned target object, target object, and object all represent objects for which three-dimensional information is pre-acquired. It can be a solid object, or it can be composed of multiple objects.
  • the three-dimensional information of the target includes a three-dimensional image, a three-dimensional point cloud, a three-dimensional grid, a local three-dimensional feature, a three-dimensional size, and all parameters with the three-dimensional feature of the target.
  • the so-called three-dimensional in the present invention refers to having three directional information of XYZ, especially having depth information, which is essentially different from having only two-dimensional plane information. It is also fundamentally different from some definitions that are called three-dimensional, panoramic, holographic, and three-dimensional, but actually only include two-dimensional information, especially not depth information.
  • the acquisition area mentioned in the present invention refers to the range that can be photographed by an image acquisition device (eg, a camera).
  • the image acquisition device in the present invention can be CCD, CMOS, camera, video camera, industrial camera, monitor, camera, mobile phone, tablet, notebook, mobile terminal, wearable device, smart glasses, smart watch, smart bracelet and Image acquisition capabilities for all devices.
  • modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
  • the modules or units or components in the embodiments may be combined into one module or unit or component, and further they may be divided into multiple sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method so disclosed may be employed in any combination, unless at least some of such features and/or procedures or elements are mutually exclusive. All processes or units of equipment are combined.
  • Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
  • Various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all of the components in the device according to the present invention according to the embodiments of the present invention.
  • DSP digital signal processor
  • the present invention can also be implemented as apparatus or apparatus programs (eg, computer programs and computer program products) for performing part or all of the methods described herein.
  • Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computer Graphics (AREA)
  • Geometry (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

一种空间占用情况判断的装置及方法，包括3D信息采集装置(6)和第一处理器；该空间内包含运动物体；3D信息采集装置(6)用于在不同时刻对空间进行扫描，获得多张能够合成三维模型的图像；第一处理器，用于将不同时刻获得的三维模型进行比较，从而判断物体对空间的占用情况。首次提出对于空间和容置于空间内的不断运动的物体进行3D建模，从而获得空间的占用情况。

Description

一种包含运动物体的空间占用率采集判断设备及方法 技术领域
本发明涉及形貌测量技术领域，特别涉及3D形貌测量技术领域。
背景技术
目前在很多场景都需要进行空间占用情况评估，例如在道路中评估车流量大小，即车辆对道路的占用情况评估；管道中液体是否已经占满整个管道等。诸如此类的需求，目前均是利用特殊传感器(例如流量)的方式实现，但这种方式制成的设备只适用于一种场景(例如流量传感器只能用于测量液体流量)。现在也出现了使用视觉传感器作为测量手段的方式。但目前均是通过采集图像进行平面图像的分析来进行判断。显然，这种方式精度较差。因此，首先目前急需一种高精度的空间占用率分析方法。
其次，在进行3D信息采集时，目前常用的方法包括使用机器视觉的方式和结构光、激光测距、激光雷达的方式。结构光、激光测距、激光雷达的方式均需要主动光源发射到目标物上，在某些情况下会对目标物造成影响，且光源成本较高。并且光源结构比较精密，易于损坏。而机器视觉的方式是采集物体不同角度的图片，并将这些图片匹配拼接形成3D模型，成本低、易使用。其在采集不同角度图片时，可以待测物不同角度设置多个相机，也可以通过单个或多个相机旋转从不同角度采集图片。但无论这两种方式哪一种，都需要将相机的采集位置围绕目标物设置(简称环绕式)，但这种方式需要较大空间为图像采集装置设置采集位置。
而且，除了单一目标物3D构建外，通常还有目标物内部空间3D模型构建需求和周边较大视场范围内的3D模型构建的需求，这是传统环绕式3D采集装置所很难做到的。
在现有技术中，也曾提出使用包括旋转角度、目标物尺寸、物距的经验公式限定相机位置，从而兼顾合成速度和效果。然而在实际应用中发现这在环绕式3D采集中是可行的，可以事先测量目标物尺寸。但在开放式的空间中则难以事先测量目标物，例如需要采集获得街道、交通路口、楼群、隧道、车流等的3D信息(不限于此)。这使得这种方法难以奏效。即使是固定的较小的目标物，例如家具、人身体部分等虽然可以事先测量其尺寸，但这种方法依然受 到较大限制：目标物尺寸难以准确确定，特别是某些应用场合目标物需要频繁更换，每次测量带来大量额外工作量，并且需要专业设备才能准确测量不规则目标物。测量的误差导致相机位置设定误差，从而会影响采集合成速度和效果；准确度和速度还需要进一步提高。
现有技术虽然也有对于环绕式采集装置优化的方法，但当3D采集合成设备的相机的采集方向与其旋转轴方向相互背离的情况时，现有技术就没有更佳的优化方法。
因此，急需一种能够精确、高效、方便采集空间3D信息，并计算其占用率情况的技术。
发明内容
鉴于上述问题，提出了本发明提供一种克服上述问题或者至少部分地解决上述问题的一种空间占用情况判断的装置及方法。
本发明实施例提供了一种空间占用情况判断的装置及方法，包括3D信息采集装置和第一处理器；
该空间内包含运动物体；
3D信息采集装置用于在不同时刻对空间进行扫描，获得多张能够合成三维模型的图像；
第一处理器，用于将不同时刻获得的三维模型进行比较，从而判断物体对空间的占用情况。
在可选的实施例中，第一处理器还用于合成三维模型。
在可选的实施例中，还包括第二处理器，3D信息采集装置包括或与该第二处理器连接，用于合成三维模型。
在可选的实施例中，3D信息采集装置采集的图像为能够合成空间的三维模型，和/或能够合成空间-物体的三维模型的多个图像。
在可选的实施例中，3D信息采集装置包括图像采集装置、旋转装置；
其中图像采集装置与旋转装置连接，由旋转装置带动其旋转；
图像采集装置在相邻的两个采集位置的光轴的夹角α满足如下条件：
Figure PCTCN2021123789-appb-000001
其中，R为旋转中心到目标物表面的距离，T为采集时物距与像距的和，d为图像采集装置的感光元件的长度或宽度，F为图像采集装置的镜头焦距，u 为经验系数。
在可选的实施例中，u<0.498，为了更佳的合成效果，优选u<0.411，特别是优选u<0.359，在一些应用场合下u<0.281，或u<0.169，或u<0.041，或u<0.028。
在可选的实施例中，图像采集装置的光学采集口均背向旋转轴方向。
在可选的实施例中，所述比较为空间三维模型与空间-物体三维模型进行比较；或为不同时刻空间-物体三维模型进行比较。
在可选的实施例中，匹配完成后，处理器将匹配结果输出至显示装置、打印装置、和/或动作执行装置。
在可选的实施例中，所述不同时刻为预设时间尺度。
发明点及技术效果
1、首次提出对于空间和容置于空间内的不断运动的物体进行3D建模，从而获得空间的占用情况，比传统的空间占用率分析技术更加通用，且更加准确。
2、首次提出通过测量旋转中心与目标物距离、图像传感元件与目标物距离的方式优化相机采集位置，从而兼顾3D构建的速度和效果。
3、通过对比分析空间3D模型和空间-物体3D模型，或是空间-物体3D模型之间的对比，能够高效、精确地得到目前空间占用率情况。
附图说明
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了本发明实施例提供的3D信息采集装置的一种实现方式的结构示意图；
图2示出了本发明实施例提供的3D信息采集装置的另一种实现方式的结构示意图。
图3示出了本发明实施例提供的3D信息采集装置的第三种实现方式的结构示意图。
图4示出了本发明实施例提供的3D信息采集装置的第四种实现方式的结构示意图。
图5示出了本发明实施例提供的3D信息采集装置对空间3D模型采集的示 意图。
图6示出了本发明实施例提供的3D信息采集装置对空间与其内的物体共同进行3D采集的示意图。
附图中的附图标记与各部件的对应关系如下：
1 图像采集装置；
2 旋转装置；
3 承载装置；
4 伸缩装置；
5 俯仰装置；
6 3D信息采集装置。
具体实施方式
下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。
3D信息采集装置结构
为解决上述技术问题，本发明提出一种3D信息采集装置，如图1所示，包括图像采集装置1、旋转装置2、承载装置3。
其中图像采集装置1与旋转装置2的旋转轴连接，由旋转装置2带动其转动。图像采集装置的采集方向为背离旋转中心方向。即采集方向为指向相对于旋转中心向外。图像采集装置的光轴可以与旋转平面平行，也可以与旋转平面成一定夹角，例如在以旋转平面为基准-90°-90°的范围内均是可以的。通常旋转轴或其延长线(即旋转中心线)通过图像采集装置，即图像采集装置仍然以自转方式转动。这与传统的图像采集装置围绕某一目标物进行旋转的采集方式(环绕式)本质不同，即与环绕目标物转动的环绕式完全不同。图像采集装置的光学采集口(例如镜头)均背向旋转轴方向，也就是说图像采集装置的采集区与旋转中心线无交集。可选的，图像采集装置的光轴与水平面具有夹角，因此这种方式与一般的自转式也有较大差别，特别是能够采集表面与水平面不垂直的目标物。
当然，旋转装置的旋转轴也可以通过减速装置与图像采集装置连接，例如通过齿轮组等。当图像采集装置在水平面进行360°的旋转时，其在特定位置拍摄对应目标物的图像(具体拍摄位置后续将详细描述)。这种拍摄可以是与旋转动作同步进行，或是在拍摄位置停止旋转后进行拍摄，拍摄完毕后继续旋转，以此类推。上述旋转装置可以为电机、马达、步进电机、伺服电机、微型马达等。旋转装置(例如各类电机)可以在控制器的控制下按照规定速度转动，并且可以转动规定角度，从而实现采集位置的优化，具体采集位置下面将详细说明。当然也可以使用现有设备中的旋转装置，将图像采集装置安装其上即可。
承载装置3用来承载整个设备的重量，旋转装置2与承载装置3连接。承载装置可以为三脚架、带有支撑装置的底座等。通常情况下，旋转装置位于承载装置的中心部分，以保证平衡。但在一些特殊场合中，也可以位于承载装置任意位置。而且承载装置并不是必须的。旋转装置可以直接安装于应用设备中，例如可以安装于车辆顶部。
另一种实施例中，如图2所示，包括图像采集装置1、旋转装置2、承载装置3、伸缩装置4。
其中图像采集装置1与旋转装置2的旋转轴连接，由旋转装置2带动其转动。当然，旋转装置的旋转轴也可以通过减速装置与图像采集装置连接，例如通过齿轮组等。当图像采集装置在水平面进行360°的旋转时，其在特定位置拍摄对应目标物的图像(具体拍摄位置后续将详细描述)。这种拍摄可以是与旋转动作同步进行，或是在拍摄位置停止旋转后进行拍摄，拍摄完毕后继续旋转，以此类推。上述旋转装置可以为电机、马达、步进电机、伺服电机、微型马达等。旋转装置(例如各类电机)可以在控制器的控制下按照规定速度转动，并且可以转动规定角度，从而实现采集位置的优化，具体采集位置下面将详细说明。当然也可以使用现有设备中的旋转装置，将图像采集装置安装其上即可。
伸缩装置4一端与旋转装置2连接，另一端与承载装置3连接，用于在沿图像采集装置光轴垂直的方向伸缩，从而可以将图像采集装置定位在不同的位置。在每个位置，再经过旋转装置驱动下进行旋转扫描，从而可以构建出该位置处目标物的3D模型。在扫描完某一位置后，伸缩装置再次运动，从而使得图像采集装置移动至另一位置，重复进行上述扫描，以此类推，即可实现细长目标物内部3D模型的构建。也可以用于在周边目标物较高时，分不同高度层次进行扫描，从而构建整个目标物的3D模型。
伸缩装置可以为伸缩套管、伸缩滑轨等多种伸缩结构。其伸缩可以手动调 节，也可以在控制单元的控制下进行伸缩。伸缩装置还可以包括伸缩电机，用于驱动伸缩单元(例如伸缩套管)延长或缩短。在伸缩到位后，可以通过锁定单元将伸缩装置长度锁定，从而为旋转装置提供稳定的支撑。锁定单元可以是机械锁定单元，例如锁定销等，也可以是电动锁定单元，例如在控制单元控制下，锁定伸缩装置。
承载装置3用来承载整个设备的重量。承载装置可以为三脚架、带有支撑装置的底座等。通常情况下，旋转装置位于承载装置的中心部分，以保证平衡。但在一些特殊场合中，也可以位于承载装置任意位置。而且承载装置并不是必须的。旋转装置可以直接安装于应用设备中，例如可以安装于行走机器人顶部。
通过伸缩装置设计，可以使得图像采集装置能够在不同高度采集信息，这样对于室内挑高较高的建筑而言，能够全面准确的采集。
在另一种实施方式中，如图3所示，3D信息采集装置包括图像采集装置1、旋转装置2、承载装置3、俯仰装置5。其中图像采集装置1设置在俯仰装置5上，使得图像采集装置1可以沿垂直面俯仰转动。俯仰装置可以为滚轮、齿轮、轴承、球节等。图像采集装置的光轴通常与俯仰方向平行，但某些特殊情况下也可以呈一定的夹角。俯仰装置可以为手动调节的，也可以在电动机驱动下俯仰转动，从而根据程序控制实现精确的俯仰角调节。俯仰装置还包括锁死机构，用于在俯仰角调节到位，图像采集装置光轴与水平面呈预定角度后，将俯仰装置锁死，从而防止其在垂直方向上再次转动。
俯仰装置5与旋转装置2的旋转轴连接，由旋转装置2带动其转动。当然，旋转装置的旋转轴也可以通过减速装置与俯仰装置连接，例如通过齿轮组等。
由于俯仰装置的调节，通常情况下图像采集装置的光轴与水平面呈一定的夹角。这样可以扫描表面与水平面不垂直的目标物。即根据目标物表面与水平面大致夹角情况，来调节俯仰装置，使得图像采集装置的光轴尽可能多地垂直目标物表面，提高对于目标物细节的采集精准度。当然，特殊情况下其也可以与水平面平行。
通过俯仰角的设计，在图像采集装置的视场不能覆盖室内所有空间，特别是上部空间时，可以适当将图像采集装置向上仰角，从而使得采集范围更大。
在另一种实施方式中，如图4所示，可以同时包括伸缩装置4和俯仰装置5。即图像采集装置1安装于俯仰装置5上，俯仰装置5与旋转装置2连接，旋转装置2安装在伸缩装置4一端，伸缩装置4另一端安装于承载装置3上。这样，在遇到室内空间较大、较高时(例如教堂)，可以通过伸缩杆依次将图像 采集装置定位在不同高度位置，再依次进行扫描采集，也可以通过调整俯仰角，使得图像采集装置采集更多的上层空间信息。当然根据情况两者可以同时使用。
对于以上所有实施方式，图像采集装置的采集方向为背离旋转中心方向。即采集方向为指向相对于旋转中心向外。图像采集装置的光轴可以与旋转平面平行，也可以与旋转平面成一定夹角，例如在以旋转平面为基准-90°-90°的范围内均是可以的。通常旋转轴或其延长线(即旋转中心线)通过图像采集装置，即图像采集装置仍然以自转方式转动。这与传统的图像采集装置围绕某一目标物进行旋转的采集方式(环绕式)本质不同，即与环绕目标物转动的环绕式完全不同。图像采集装置的光学采集口(例如镜头)均背向旋转轴方向，也就是说图像采集装置的采集区与旋转中心线无交集。同时由于图像采集装置的光轴与水平面具有夹角，因此这种方式与一般的自转式也有较大差别，特别是能够采集表面与水平面不垂直的目标物。
上述装置还可以包括测距装置，测距装置与图像采集装置固定连接，且测距装置指向方向与图像采集装置光轴方向相同。当然测距装置也可以固定连接于旋转装置上，只要可以随图像采集装置同步转动即可。优选的，可以设置安装平台，图像采集装置和测距装置均位于平台上，平台安装于旋转装置旋转轴上，由旋转装置驱动转动。测距装置可以使用激光测距仪、超声测距仪、电磁波测距仪等多种方式，也可以使用传统的机械量具测距装置。当然，在某些应用场合中，3D采集装置位于特定位置，其与目标物的距离已经标定，无需额外测量。
还可以包括光源，光源可以设置于图像采集装置周边、旋转装置上以及安装平台上。当然光源也可以单独设置，例如使用独立光源照射目标物。甚至在光照条件较好的时候不使用光源。光源可以为LED光源，也可以为智能光源，即根据目标物及环境光的情况自动调整光源参数。通常情况下，光源位于图像采集装置的镜头周边分散式分布，例如光源为在镜头周边的环形LED灯。由于在一些应用中需要控制光源强度。特别是可以在光源的光路上设置柔光装置，例如为柔光外壳。或者直接采用LED面光源，不仅光线比较柔和，而且发光更为均匀。更佳地，可以采用OLED光源，体积更小，光线更加柔和，并且具有柔性特性，可以贴附于弯曲的表面。
为了方便目标物的实际尺寸测量，可在目标物位置设置多个标记点。并且 这些标记点的坐标已知。通过采集标记点，并结合其坐标，获得3D合成模型的绝对尺寸。这些标记点可以为事先设置的点，也可以是激光光点。确定这些点的坐标的方法可以包括：①使用激光测距：使用标定装置向着目标物发射激光，形成多个标定点光斑，通过标定装置中激光测距单元的已知位置关系获得标定点坐标。使用标定装置向着目标物发射激光，使得标定装置中的激光测距单元发射的光束落在目标物上形成光斑。由于激光测距单元发射的激光束相互平行，且各个单元之间的位置关系已知。那么在目标物上形成的多个光斑的在发射平面的二维坐标就可以得到。通过激光测距单元发射的激光束进行测量，可以获得每个激光测距单元与对应光斑之间的距离，即相当于在目标物上形成的多个光斑的深度信息可以获得。即垂直于发射平面的深度坐标就可以得到。由此，可以获得每个光斑的三维坐标。②使用测距与测角结合：分别测量多个标记点的距离以及相互之间的夹角，从而算出各自坐标。③使用其它坐标测量工具：例如RTK、全球坐标定位系统、星敏定位系统、位置和位姿传感器等。
3D信息采集监控流程
1、空间3D模型采集
请参考图5：
在空间内不存在要监控的物体时，此时可以获得空间的3D模型。具体方式如下：
设置3D采集装置6位置，使得其视场扫描范围覆盖空间，且主要覆盖该空间。例如，在监控某一道路车流量时，可以将道路路面及其上一定高度的区域均认为是该空间。而将路上行驶的每个车认为是相应物体。当采集装置安装较高，而待采集的空间位于下方时，需要将设备的图像采集装置转至某一俯角再进行采集。
旋转装置按一定速度带动图像采集装置进行旋转，在旋转过程中图像采集装置在设定好的位置进行图像采集。此时可以不停止旋转，即图像采集与旋转同步进行；也可以在待采集的位置停止旋转，进行图像采集，采集完毕后继续旋转至下一个待采集位置。旋转装置可以利用事先设定好的控制单元中的程序进行驱动。也可以通过通讯接口与上位机进行通讯，通过上位机进行控制旋转。特别是其还可以与移动终端通过有线或无线进行连接，通过移动终端(例如手机)控制旋转装置转动。即可以通过远程平台、云平台、服务器、上位机、移动终端设置旋转装置转动参数，控制其旋转的启停。
图像采集装置采集到目标物多张图像，并将图像通过通讯装置送入远程平台、云平台、服务器、上位机和/或移动终端中，利用3D模型合成方法进行目标空间内部的3D合成。
对于需要在多个高度进行采集的空间，可以采用如下流程。
控制伸缩装置长度使得图像采集装置位于预定位置，旋转装置按一定速度带动图像采集装置进行旋转，在旋转过程中图像采集装置在设定好的位置进行图像采集。此时可以不停止旋转，即图像采集与旋转同步进行；也可以在待采集的位置停止旋转，进行图像采集，采集完毕后继续旋转至下一个待采集位置。旋转装置可以利用事先设定好的控制单元中的程序进行驱动。也可以通过通讯接口与上位机进行通讯，通过上位机进行控制旋转。特别是其还可以与移动终端通过有线或无线进行连接，通过移动终端(例如手机)控制旋转装置转动。即可以通过远程平台、云平台、服务器、上位机、移动终端设置旋转装置转动参数，控制其旋转的启停。
控制伸缩装置长度使得图像采集装置位于另一预定位置，重复上述旋转装置动作，从而使得图像采集装置可以采集环绕该位置的目标物图像，依次类推，在多个高度位置进行采集，获得图像，从而构建对应的3D模型。
图像采集装置采集到目标物多张图像，并将图像通过通讯装置送入远程平台、云平台、服务器、上位机和/或移动终端中，利用3D模型合成方法进行目标物的3D合成。
当然，也可以控制俯仰装置，使得图像采集装置俯仰至一定角度后，再进行旋转采集。例如在监控某一道路车流量、某一水渠水流量时，通常将采集装置安装在上面，此时需要图像采集装置转动一定的俯角再进行扫描。
特别的，可以在采集前或者采集的同时，使用测距装置测量相关公式条件中相应的距离参数，即旋转中心到目标物的距离、传感元件到目标物的距离。根据相应条件公式计算出采集位置，并提示给用户进行旋转参数的设定，或自动设定旋转参数。
在采集前进行测距时，可以使得旋转装置带动测距装置转动，从而测量不同位置上上述两个距离。并对多个测量点测得的两个距离分别取平均值，作为本次采集的统一距离值带入公式中。所述平均值的获得可以使用求和平均的方式，也可以使用加权平均的方式，还可以使用其它求均值的方式，或舍弃异常值再平均的方式等。
在采集过程中进行测距时，在旋转装置转动到第一位置进行图像采集的同 时，进行上述两个距离值的测量，并将它们带入条件公式中计算间隔角度，根据该角度确定下一采集位置。
2、空间与其内的物体共同进行3D采集
在空间内存在待监测的物体时，此时可以获得物体和空间共同的3D模型，以下称为空间-物体3D模型。具体方式与前述一致，仅是目标从单一的空间变成了空间和容置于空间内的物体。同时，由于物体是运动的，因此设备需要在不同时刻采集，从而获得多个时刻空间与物体的3D模型。
(1)设置3D采集装置6位置，使得其视场扫描范围同时覆盖空间和物体，且主要覆盖该空间和物体。例如，在监控某一道路车流量时，可以将道路路面及其上一定高度的区域均认为是该空间，而将路上行驶的每个车认为是相应物体，采集装置可以同时扫描到路面空间和行驶车辆的图像。当采集装置安装较高，而待采集的空间位于下方时，需要将设备的图像采集装置转至某一俯角再进行采集。
(2)在T0时刻，采集装置采集多个空间-物体的图像，并给每个图像打上时间戳，然后进行3D合成，从而获得多个空间-物体的3D模型。具体采集方法与上述空间采集一致，不再赘述。
(3)在T1时刻，采集装置采集多个空间-物体的图像，并给每个图像打上时间戳，然后进行3D合成，从而再次获得多个空间-物体的3D模型。可以理解，T0和T1时间间隔可以较短，也可以根据需要较长。最短的间隔为：采集装置一直进行旋转不停，从而不间断地采集。
(4)以此类推，在不同时刻分别采集空间-物体的图像，并分别进行3D合成，从而不断获得多个目标物的3D模型。
3、物体占用空间的判断
利用Tn时刻采集获得的空间-物体的3D模型和采集获得的空间的3D模型进行三维比较，从而得到该时刻空间被物体占用的程度。例如，采集空的街道的3D模型，再在某时刻采集有车流量的街道3D模型，两者进行比较即可得到此时街道被车辆占用比例。
当然，也可以用利用Tn时刻采集获得的空间-物体的3D模型和Tm时刻采集获得的空间-物体的3D模型进行三维比较，从而得到两个不同时刻空间被占用的比例变化。
以上判断的过程可以在采集装置中完成，也可以在服务器、云平台中完成。也就是说，任何一个采集装置可以独立地获得目标物的图像，并在其内部完成 3D合成，及不同时刻3D模型的比较判断，将判断结果发送给服务器或用户。但也可以仅在采集装置中完成图像采集和3D合成，在远程处理器、服务器或云平台上完成判断。另外一种情况，每个设备都可以独立采集目标物的图像，但通过通讯网络将其传输至远程处理器、服务器或云平台上，在它们上面合成目标物3D模型，并完成3D模型的比较判断。这样可以极大降低每个采集装置的成本。
可以理解，上述扫描和判断可以是针对一个空间-物体，也可以针对多个空间-物体。
相机位置的优化
为了保证设备能够兼顾3D合成的效果和效率，除了常规的优化合成算法的方法外，还可以通过优化相机采集位置的方法。特别是当3D采集合成设备的相机的采集方向与其旋转轴方向相互背离的情况时，对于这种设备现有技术未提到如何进行相机位置的更佳的优化。即使存在的一些优化方法，其也是在不同实验下得到的不同的经验条件。特别是，现有的一些位置优化方法需要获得目标物的尺寸，这在环绕式3D采集中是可行的，可以事先测量完毕。但在开放式的空间中则难以事先测量得到。因此需要提出一种能够适用于当3D采集合成设备的相机的采集方向与其旋转轴方向相互背离的情况时进行相机位置优化的方法。这正是本发明所要解决的问题，和做出的技术贡献。
为此，本发明进行了大量实验，总结出在进行采集时相机采集的间隔优选满足的经验条件如下。
在进行3D采集时，图像采集装置在相邻的两个位置时其光轴的夹角α满足如下条件：
Figure PCTCN2021123789-appb-000002
其中，
R为旋转中心到目标物表面的距离，
T为采集时物距与像距的和，也就是图像采集装置的感光单元与目标物的距离。
d为图像采集装置的感光元件(CCD)的长度或宽度，当上述两个位置是沿感光元件长度方向时，d取矩形长度；当上述两个位置是沿感光元件宽度方 向时，d取矩形宽度。
F为图像采集装置的镜头焦距。
u为经验系数。
通常情况下，在采集装置上配置有测距装置，例如激光测距仪。将其光轴与图像采集装置的光轴调节平行，则其可以测量采集装置到目标物表面的距离，利用测量得到的距离，根据测距装置与采集装置各部件的已知位置关系，即可获得R和T。
图像采集装置在两个位置中的任何一个位置时，感光元件沿着光轴到目标物表面的距离作为T。除了这种方法外，也可以使用多次平均法或其他方法，其原则是T的值应当与采集时像距物距和不背离。
同样道理，图像采集装置在两个位置中的任何一个位置时，旋转中心沿着光轴到目标物表面的距离作为R。除了这种方法外，也可以使用多次平均法或其他方法，其原则是R的值应当与采集时旋转半径不背离。
通常情况下，现有技术中均采用物体尺寸作为推算相机位置的方式。由于物体尺寸会随着测量物体的变化而改变。例如，在进行一个大物体3D信息采集后，再进行小物体采集时，就需要重新测量尺寸，重新推算。上述不方便的测量以及多次重新测量都会带来测量的误差，从而导致相机位置推算错误。而本方案根据大量实验数据，给出了相机位置需要满足的经验条件，不需要直接测量物体大小尺寸。经验条件中d、F均为相机固定参数，在购买相机、镜头时，厂家即会给出相应参数，无需测量。而R、T仅为一个直线距离，用传统测量方法，例如直尺、激光测距仪均可以很便捷的测量得到。同时，由于本发明的设备中，图像采集装置(例如相机)的采集方向与其旋转轴方向相互背离，也就是说，镜头朝向与旋转中心大体相反。此时控制图像采集装置两次位置的光轴夹角α就更加容易，只需要控制旋转驱动电机的转角即可。因此，使用α来定义最优位置是更为合理的。因此，本发明的经验公式使得准备过程变得方便快捷，同时也提高了相机位置的排布准确度，使得相机能够设置在优化的位置中，从而在同时兼顾了3D合成精度和速度。
根据大量实验，为保证合成的速度和效果，u应当小于0.498，为了更佳的合成效果，优选u<0.411，特别是优选u<0.359，在一些应用场合下u<0.281，或u<0.169，或u<0.041，或u<0.028。
利用本发明装置，进行实验，部分实验数据如下所示，单位mm。(以下数据仅为有限举例)
Figure PCTCN2021123789-appb-000003
以上数据仅为验证该公式条件所做实验得到的，并不对发明构成限定。即使没有这些数据，也不影响该公式的客观性。本领域技术人员可以根据需要调整设备参数和步骤细节进行实验，得到其他数据也是符合该公式条件的。
3D模型合成方法
图像采集装置采集获得的多个图像送入处理单元中，利用下述算法构建3D模型。所述处理单元可以位于采集设备中，也可以位于远程，例如云平台、服务器、上位机等。
具体算法主要包括如下步骤：
步骤1：对所有输入照片进行图像增强处理。采用下述滤波器增强原始照片的反差和同时压制噪声。
Figure PCTCN2021123789-appb-000004
式中：g(x，y)为原始影像在(x，y)处灰度值，f(x，y)为经过Wallis滤波器增强后该处的灰度值，m g为原始影像局部灰度均值，s g为原始影像局部灰度标准偏差，m f为变换后的影像局部灰度目标值，s f为变换后影像局部灰度标准偏差目标值。c∈(0，1)为影像方差的扩展常数，b∈(0，1)为影像亮度系数常数。
该滤波器可以大大增强影像中不同尺度的影像纹理模式，所以在提取影像的点特征时可以提高特征点的数量和精度，在照片特征匹配中则提高了匹配结果可靠性和精度。
步骤2：对输入的所有照片进行特征点提取，并进行特征点匹配，获取稀疏特征点。采用SURF算子对照片进行特征点提取与匹配。SURF特征匹配方法主要包含三个过程，特征点检测、特征点描述和特征点匹配。该方法使用Hessian矩阵来检测特征点，用箱式滤波器(Box Filters)来代替二阶高斯滤波，用积分图像来加速卷积以提高计算速度，并减少了局部影像特征描述符的维数，来加快匹配速度。主要步骤包括①构建Hessian矩阵，生成所有的兴趣点，用于特征提取，构建Hessian矩阵的目的是为了生成图像稳定的边缘点(突变点)；②构建尺度空间特征点定位，将经过Hessian矩阵处理的每个像素点与二维图像空间和尺度空间邻域内的26个点进行比较，初步定位出关键点，再经过滤除能量比较弱的关键点以及错误定位的关键点，筛选出最终的稳定的特征点；③特征点主方向的确定，采用的是统计特征点圆形邻域内的harr小波特征。即在特征点的圆形邻域内，统计60度扇形内所有点的水平、垂直harr小波特征总和，然后扇形以0.2弧度大小的间隔进行旋转并再次统计该区域内harr小波特征值之后，最后将值最大的那个扇形的方向作为该特征点的主方向；④生成64维特征点描述向量,特征点周围取一个4*4的矩形区域块，但是所取得矩形区域方向是沿着特征点的主方向。每个子区域统计25个像素的水平方向和垂直方向的haar小波特征，这里的水平和垂直方向都是相对主方向而言的。该haar小波特征为水平方向值之后、垂直方向值之后、水平方向绝对值之后以及垂直方向绝对值之和4个方向，把这4个值作为每个子块区域的特征向量，所以一共有4*4*4＝64维向量作为Surf特征的描述子；⑤特征点匹配，通过计算两个特征点间的欧式距离来确定匹配度，欧氏距离越短，代表两个特征点的匹配度越好。
步骤3：输入匹配的特征点坐标，利用光束法平差，解算稀疏的目标物三维点云和拍照相机的位置和姿态数据，即获得了稀疏目标物模型三维点云和位置的模型坐标值；以稀疏特征点为初值，进行多视照片稠密匹配，获取得到密集点云数据。该过程主要有四个步骤：立体像对选择、深度图计算、深度图优化、深度图融合。针对输入数据集里的每一张影像，我们选择一张参考影像形成一个立体像对，用于计算深度图。因此我们可以得到所有影像的粗略的深度图，这些深度图可能包含噪声和错误，我们利用它的邻域深度图进行一致性检查，来优化每一张影像的深度图。最后进行深度图融合，得到整个场景的三维点云。
步骤4：利用密集点云进行目标物曲面重建。包括定义八叉树、设置函数 空间、创建向量场、求解泊松方程、提取等值面几个过程。由梯度关系得到采样点和指示函数的积分关系，根据积分关系获得点云的向量场，计算指示函数梯度场的逼近，构成泊松方程。根据泊松方程使用矩阵迭代求出近似解，采用移动方体算法提取等值面，对所测点云重构出被测物体的模型。
步骤5：目标物模型的全自动纹理贴图。表面模型构建完成后，进行纹理贴图。主要过程包括：①纹理数据获取通过图像重建目标的表面三角面格网；②重建模型三角面的可见性分析。利用图像的标定信息计算每个三角面的可见图像集以及最优参考图像；③三角面聚类生成纹理贴片。根据三角面的可见图像集、最优参考图像以及三角面的邻域拓扑关系，将三角面聚类生成为若干参考图像纹理贴片；④纹理贴片自动排序生成纹理图像。对生成的纹理贴片，按照其大小关系进行排序，生成包围面积最小的纹理图像，得到每个三角面的纹理映射坐标。
应当注意，上述算法是本发明使用的算法，本算法与图像采集条件相互配合，使用该算法兼顾了合成的时间和质量。但可以理解，同样可以使用现有技术中常规3D合成算法也可以与本发明的方案进行配合使用。
应用实例
除了上述街道车流量的监控，还可以利用本发明的方法监控水渠水流量。同时对于运动物体而言，可以是更大尺度时间尺度的运动，例如山体的变形。
在枯水期利用上述设备和方法获得河堤和水流的3D模型，在洪水期间隔30分钟不断获得该时刻河堤和水流的3D模型，并将其与枯水期3D模型相比较，从而判断水流占用河道情况，即可以对洪水、溃堤提前预警。
另外，上面是各个采集装置分别获得3D模型再导入处理器进行判断，也可以直接将各个采集装置的图片直接导入处理器，在处理器中集中完成3D模型的合成，以及它们之间的比较判断。这样可以简化采集硬件的结构和成本。例如在采集装置中不需要设置能够实现大数据处理的处理器，只需要进行简单的控制即可。而采集的图像通过4G、5G或其他通讯网络可以传输至云平台(相当于一种处理器)中进行集中处理。这也是本发明的发明点之一。
通过这种方式，可以监控任意空间内流动物体对于空间的占用情况。并且由于这种比较和判断是基于三维信息而做出的，比二维图像判断更加准确。
比较判断完成后，处理器将判断结果输出至显示装置进行显示，或提示用户，例如输出至电脑、手机等移动终端界面；或输出至打印装置，进行2D或 3D打印，便于现场操作观看使用；也可以直接与动作机构连接，例如直接与红绿灯连接，控制该街道的红绿灯。
上述目标物体、目标物、及物体皆表示预获取三维信息的对象。可以为一实体物体，也可以为多个物体组成物。所述目标物的三维信息包括三维图像、三维点云、三维网格、局部三维特征、三维尺寸及一切带有目标物三维特征的参数。本发明里所谓的三维是指具有XYZ三个方向信息，特别是具有深度信息，与只有二维平面信息具有本质区别。也与一些称为三维、全景、全息、三维，但实际上只包括二维信息，特别是不包括深度信息的定义有本质区别。
本发明所说的采集区域是指图像采集装置(例如相机)能够拍摄的范围。本发明中的图像采集装置可以为CCD、CMOS、相机、摄像机、工业相机、监视器、摄像头、手机、平板、笔记本、移动终端、可穿戴设备、智能眼镜、智能手表、智能手环以及带有图像采集功能所有设备。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本公开并帮助理解各个发明方面中的一个或多个，在上面对本发明的示例性实施例的描述中，本发明的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该公开的方法解释成反映如下意图：即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如下面的权利要求书所反映的那样，发明方面在于少于前面公开的单个实施例的所有特征。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本发明的单独实施例。
本领域那些技术人员可以理解，可以对实施例中的设备中的模块进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的模块或单元或组件组合成一个模块或单元或组件，以及此外可以把它们分成多个子模块或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例的基于本发明装置中的一些或者全部部件的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
至此，本领域技术人员应认识到，虽然本文已详尽示出和描述了本发明的多个示例性实施例，但是，在不脱离本发明精神和范围的情况下，仍可根据本发明公开的内容直接确定或推导出符合本发明原理的许多其他变型或修改。因此，本发明的范围应被理解和认定为覆盖了所有这些其他变型或修改。

Claims (10)

  1. 一种空间占用情况判断的装置及方法，其特征在于：包括3D信息采集装置和第一处理器；
    该空间内包含运动物体；
    3D信息采集装置用于在不同时刻对空间进行扫描，获得多张能够合成三维模型的图像；
    第一处理器，用于将不同时刻获得的三维模型进行比较，从而判断物体对空间的占用情况。
  2. 如权利要求1所述的装置及方法，其特征在于：第一处理器还用于合成三维模型。
  3. 如权利要求1所述的装置及方法，其特征在于：还包括第二处理器，3D信息采集装置包括或与该第二处理器连接，用于合成三维模型。
  4. 如权利要求1所述的装置及方法，其特征在于：3D信息采集装置采集的图像为能够合成空间的三维模型，和/或能够合成空间-物体的三维模型的多个图像。
  5. 如权利要求1所述的装置及方法，其特征在于：3D信息采集装置包括图像采集装置、旋转装置；
    其中图像采集装置与旋转装置连接，由旋转装置带动其旋转；
    图像采集装置在相邻的两个采集位置的光轴的夹角α满足如下条件：
    Figure PCTCN2021123789-appb-100001
    其中，R为旋转中心到目标物表面的距离，T为采集时物距与像距的和，d为图像采集装置的感光元件的长度或宽度，F为图像采集装置的镜头焦距，u为经验系数。
  6. 如权利要求5所述的装置及方法，其特征在于：u<0.498，为了更佳的合成效果，优选u<0.411，特别是优选u<0.359，在一些应用场合下u<0.281，或u<0.169，或u<0.041，或u<0.028。
  7. 如权利要求1所述的装置及方法，其特征在于：图像采集装置的光学采集口均背向旋转轴方向。
  8. 如权利要求1所述的装置及方法，其特征在于：所述比较为空间三维模型与空间-物体三维模型进行比较；或为不同时刻空间-物体三维模型进行比较。
  9. 如权利要求1所述的装置及方法，其特征在于：匹配完成后，处理器将匹配结果输出至显示装置、打印装置、和/或动作执行装置。
  10. 如权利要求1所述的装置及方法，其特征在于：所述不同时刻为预设时间尺度。
PCT/CN2021/123789 2020-10-15 2021-10-14 一种包含运动物体的空间占用率采集判断设备及方法 WO2022078440A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011105314.XA CN112254675B (zh) 2020-10-15 2020-10-15 一种包含运动物体的空间占用率采集判断设备及方法
CN202011105314.X 2020-10-15

Publications (1)

Publication Number Publication Date
WO2022078440A1 true WO2022078440A1 (zh) 2022-04-21

Family

ID=74243531

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/123789 WO2022078440A1 (zh) 2020-10-15 2021-10-14 一种包含运动物体的空间占用率采集判断设备及方法

Country Status (2)

Country Link
CN (1) CN112254675B (zh)
WO (1) WO2022078440A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112254675B (zh) * 2020-10-15 2023-04-11 天目爱视（北京）科技有限公司 一种包含运动物体的空间占用率采集判断设备及方法
CN112257536B (zh) * 2020-10-15 2022-05-20 天目爱视（北京）科技有限公司 一种空间与物体三维信息采集匹配设备及方法
CN113112589B (zh) * 2021-04-13 2022-09-02 哈尔滨工程大学 基于空间占用概率融合的增量式遥感图像的三维重建方法

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5563930B2 (ja) * 2010-08-27 2014-07-30 倉敷紡績株式会社 非接触三次元計測装置及び非接触三次元計測方法
CN105264336A (zh) * 2013-04-05 2016-01-20 洛克希德马丁公司 具有lidar的水下平台和相关方法
CN107431800A (zh) * 2015-02-12 2017-12-01 奈克斯特Vr股份有限公司 用于进行环境测量和/或使用此类测量的方法和装置
CN109218702A (zh) * 2018-09-05 2019-01-15 天目爱视（北京）科技有限公司 一种相机自转式3d测量及信息获取装置
CN109801374A (zh) * 2019-01-14 2019-05-24 盾钰（上海）互联网科技有限公司 一种通过多角度图像集重构三维模型的方法、介质及系统
US20190251331A1 (en) * 2018-02-15 2019-08-15 DMAI, Inc. System and method for reconstructing unoccupied 3d space
CN111462304A (zh) * 2020-03-16 2020-07-28 天目爱视（北京）科技有限公司 一种用于太空领域的3d采集和尺寸测量方法
CN112254675A (zh) * 2020-10-15 2021-01-22 天目爱视（北京）科技有限公司 一种包含运动物体的空间占用率采集判断设备及方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10038838B2 (en) * 2015-05-29 2018-07-31 Hover Inc. Directed image capture
CN105423943B (zh) * 2015-10-30 2017-12-15 南京巨鲨显示科技有限公司 高速三维显微成像系统及方法
CN107990839A (zh) * 2017-11-14 2018-05-04 中国科学院上海光学精密机械研究所 长条状光学元件面形的快速拼接检测装置和测量方法
CN207600384U (zh) * 2017-11-24 2018-07-10 深古安地智能科技（武汉）有限公司 一种基于线激光的集装箱容积占用率测算系统
CN109190634A (zh) * 2018-07-18 2019-01-11 南昌大学 一种高效测量鸟巢隐蔽度的方法
CN109801365A (zh) * 2019-01-25 2019-05-24 上海小萌科技有限公司 一种三维建模数据采集装置及其三维建模方法
CN111462213B (zh) * 2020-03-16 2021-07-13 天目爱视（北京）科技有限公司 一种在运动过程中获取物体3d坐标及尺寸的设备及方法

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5563930B2 (ja) * 2010-08-27 2014-07-30 倉敷紡績株式会社 非接触三次元計測装置及び非接触三次元計測方法
CN105264336A (zh) * 2013-04-05 2016-01-20 洛克希德马丁公司 具有lidar的水下平台和相关方法
CN107431800A (zh) * 2015-02-12 2017-12-01 奈克斯特Vr股份有限公司 用于进行环境测量和/或使用此类测量的方法和装置
US20190251331A1 (en) * 2018-02-15 2019-08-15 DMAI, Inc. System and method for reconstructing unoccupied 3d space
CN109218702A (zh) * 2018-09-05 2019-01-15 天目爱视（北京）科技有限公司 一种相机自转式3d测量及信息获取装置
CN111060024A (zh) * 2018-09-05 2020-04-24 天目爱视（北京）科技有限公司 旋转中心轴与图像采集装置相交的3d测量及获取装置
CN109801374A (zh) * 2019-01-14 2019-05-24 盾钰（上海）互联网科技有限公司 一种通过多角度图像集重构三维模型的方法、介质及系统
CN111462304A (zh) * 2020-03-16 2020-07-28 天目爱视（北京）科技有限公司 一种用于太空领域的3d采集和尺寸测量方法
CN112254675A (zh) * 2020-10-15 2021-01-22 天目爱视（北京）科技有限公司 一种包含运动物体的空间占用率采集判断设备及方法

Also Published As

Publication number Publication date
CN112254675A (zh) 2021-01-22
CN112254675B (zh) 2023-04-11

Similar Documents

Publication Publication Date Title
WO2022078440A1 (zh) 一种包含运动物体的空间占用率采集判断设备及方法
WO2022078442A1 (zh) 一种基于光扫描和智能视觉融合的3d信息采集方法
WO2022111105A1 (zh) 一种自由姿态的智能视觉3d信息采集设备
WO2022078439A1 (zh) 一种空间与物体三维信息采集匹配设备及方法
WO2022078418A1 (zh) 一种转动稳定的智能三维信息采集设备
CN112361962B (zh) 一种多俯仰角度的智能视觉3d信息采集设备
CN112254680B (zh) 一种多自由度的智能视觉3d信息采集设备
CN112257537B (zh) 一种智能多点三维信息采集设备
CN112254638B (zh) 一种可俯仰调节的智能视觉3d信息采集设备
CN112253913B (zh) 一种与旋转中心偏离的智能视觉3d信息采集设备
CN112254676B (zh) 一种便携式智能3d信息采集设备
CN112082486B (zh) 一种手持式智能3d信息采集设备
WO2022078419A1 (zh) 一种多偏置角度的智能视觉3d信息采集设备
WO2022078438A1 (zh) 一种室内3d信息采集设备
WO2022111104A1 (zh) 一种多翻滚角度的智能视觉3d信息采集设备
WO2022078437A1 (zh) 一种移动物体之间的三维处理设备及方法
WO2022078433A1 (zh) 一种多点组合式3d采集系统及方法
CN112254673B (zh) 一种自转式智能视觉3d信息采集设备
CN112254671B (zh) 一种多次组合式3d采集系统及方法
CN112257535B (zh) 一种躲避物体的三维匹配的设备及方法
CN112254677B (zh) 一种基于手持设备的多位置组合式3d采集系统及方法
CN112254679A (zh) 一种多位置组合式3d采集系统及方法
WO2022078421A1 (zh) 一种多俯仰角度的智能视觉3d信息采集设备
CN112304250A (zh) 一种移动物体之间的三维匹配设备及方法
WO2022078417A1 (zh) 一种自转式智能视觉3d信息采集设备

Legal Events

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

Ref document number: 21879483

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21879483

Country of ref document: EP

Kind code of ref document: A1