CN109002800A - The real-time identification mechanism of objective and recognition methods based on Multi-sensor Fusion - Google Patents

The real-time identification mechanism of objective and recognition methods based on Multi-sensor Fusion Download PDF

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CN109002800A
CN109002800A CN201810800374.XA CN201810800374A CN109002800A CN 109002800 A CN109002800 A CN 109002800A CN 201810800374 A CN201810800374 A CN 201810800374A CN 109002800 A CN109002800 A CN 109002800A
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陈刚
王振宇
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Suzhou Robot Technology Co Ltd
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/60Type of objects
    • G06V20/64Three-dimensional objects
    • G06V20/647Three-dimensional objects by matching two-dimensional images to three-dimensional objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
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    • G06F18/25Fusion techniques
    • G06F18/251Fusion techniques of input or preprocessed data
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/56Extraction of image or video features relating to colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07Target detection

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Abstract

The present invention relates to a kind of real-time identification mechanism of objective based on Multi-sensor Fusion and recognition methods.Identification mechanism includes mechanism outer box, laser radar, monocular high definition RGB camera, calculates mainboard.Recognition methods: the three-dimensional information of analysis output object is carried out by the data of laser radar;Recognition result based on step 2 carries out processing analysis, the two-dimensional signal of output identification object to the data of monocular high definition RGB camera;Two-dimensional signal is mapped in three-dimensional information, two-dimentional three-dimensional information is merged with Kalman filtering, the real-time identification information of object is robustly obtained with acceleration models such as three-dimensional Kalmans.A kind of real-time identification mechanism of objective based on Multi-sensor Fusion of the invention, using multiple RGB cameras and laser radar apparatus, realize the real-time identification function of target of Multi-sensor Fusion, the path based on target object in identifying system coordinate system is obtained in real time, position, speed, directional information, result visualization.

Description

The real-time identification mechanism of objective and recognition methods based on Multi-sensor Fusion
Technical field
The present invention relates to computer vision fields, real-time more particularly to a kind of objective based on Multi-sensor Fusion Identification mechanism and recognition methods.
Background technique
Existing multi-sensor fusion system only provides the mapping result between two dimension, three-dimensional, could not combine and provide with algorithm , dedicated for the multi-sensor fusion system identified in real time, it is long that there are deployment cycles in actual application for a whole set of, uses difficulty Spend big disadvantage;Moreover, existing target identification method is all based on single-sensor, the range information of laser radar can not be merged With the colouring information of RGB camera image, real-time recognition accuracy is low.
Summary of the invention
The present invention provides a kind of real-time identification mechanism of objective based on Multi-sensor Fusion and recognition methods, use Multiple RGB cameras and laser radar apparatus realize the real-time identification function of the target of Multi-sensor Fusion, by handling and merging two Image, three dimensional point cloud are tieed up, obtains the path based on target object in identifying system coordinate system, position, speed, direction in real time As a result information can be visualized in the form of polygon outline border.
The real-time identification mechanism of objective based on Multi-sensor Fusion, it includes mechanism outer box;The mechanism outer box are Regular pentagon box body;Laser radar is installed at the top of the mechanism outer box, is identified in real time for the objective in PCD;Institute The middle position for stating five sides of mechanism outer box is separately installed with monocular high definition RGB camera, real for the two dimension target in RGB image When identify;The inside of the mechanism outer box is equipped with calculating mainboard, in collect record and handle monocular high definition RGB camera and The data of laser radar export real-time recognition result.
The monocular high definition RGB camera generates image with the rate of 30Hz, and is pressed with 1920 × 1080 resolution ratio Contracting.
The laser radar generates data with the frequency of 10Hz, with 360 degree of full filed.
The capture range of the laser radar is 0.9-130m.
The data for calculating mainboard and receiving monocular high definition RGB camera and laser radar with 2,000,000,000 bytes/minute.
Objective real-time identification method based on Multi-sensor Fusion, specifically carries out according to the following steps:
One, laser radar, monocular high definition RGB camera are connect with mainboard is calculated, configures laser radar and camera calibration text Part;
Two, it is analyzed by the data of laser radar, by the PCD of input, excludes ground point cloud data, and based on equal The PCD of value drift is detected and positioning, exports the three-dimensional bezel locations information and tracking information of object;
Three, based on the recognition result of step 2, processing point is carried out to the two-dimensional image data result of monocular high definition RGB camera Analysis, and the detection of the RGB image based on average drifting and positioning, the two-dimentional bezel locations information and tracking letter of output identification object Breath;
Four, the two-dimensional signal of step 3 is mapped in the three-dimensional information of step 2, with Kalman filtering by two dimension three Information fusion is tieed up, the real-time identification information of object is robustly obtained with acceleration models such as three-dimensional Kalmans, that is, completes to know in real time Not.
In step 2, ground point cloud data are excluded, ground point cloud information is removed with Density Estimator.Blocked by combining The equal acceleration models of Kalman Filtering increase the robustness of removal ground point figure information, define angle searching threshold by engineering experience Value reduces the influence of outlier, and the point cloud data after processing detects track algorithm by average drifting, output identifies object Three-dimensional bezel locations information and tracking information.
In step 3, obtained three-dimensional frame information MAP is obtained into two-dimensional convex closure information into two dimensional image.Two dimension Convex closure amplifies by ratio, by the calculating of log-likelihood ratio, obtains object discrimination color model;In Convex range, pass through Average drifting detects track algorithm, the two-dimentional bezel locations information and tracking information of output identification object.
In step 3, influence of the brightness to image is reduced with the equal acceleration models of one-dimensional combination Kalman filtering.
In step 3, RGB image detection and positioning based on average drifting, the object inspection of the next frame based on average drifting The mass center for surveying and positioning the mapping since present frame.
In step 4, Kalman filtering is for merging and tracking the object mass center obtained from image and PCD, using based on card The Fusion Model of Kalman Filtering integrates the mass center of calculating, using acceleration models such as three-dimensional Kalmans for steadily tracking With fusion mass center.
Advantages of the present invention: a kind of real-time identification mechanism of objective based on Multi-sensor Fusion of the invention uses Multiple RGB cameras and laser radar apparatus, realize the real-time identification function of the target of Multi-sensor Fusion, and recognition methods passes through processing And two dimensional image, three dimensional point cloud are merged, and obtain the path based on target object in identifying system coordinate system in real time, position, As a result speed, directional information can be visualized in the form of polygon outline border.
Detailed description of the invention
Fig. 1 is a kind of schematic diagram of the real-time identification mechanism of objective based on Multi-sensor Fusion of embodiment;
Fig. 2 is a kind of another angle signal of the real-time identification mechanism of objective based on Multi-sensor Fusion of embodiment Figure;
Fig. 3 is a kind of showing for the internal structure of the real-time identification mechanism of objective based on Multi-sensor Fusion of embodiment It is intended to;
Fig. 4 is a kind of flow chart of objective real-time identification method based on Multi-sensor Fusion of embodiment.
Specific embodiment
In order to deepen the understanding of the present invention, the present invention is done below in conjunction with drawings and examples and is further retouched in detail It states, the embodiment is only for explaining the present invention, does not constitute and limits to protection scope of the present invention.
Embodiment
As shown in Figure 1 to Figure 3, a kind of real-time cognitron of the objective based on Multi-sensor Fusion is present embodiments provided Structure, it includes mechanism outer box 1;The mechanism outer box 1 are regular pentagon box body;The top of the mechanism outer box 1 is equipped with laser Radar 2 identifies in real time for the objective in PCD;The middle position on five sides of the mechanism outer box 1 is separately installed with monocular High definition RGB camera 3 identifies in real time for the two dimension target in RGB image;The inside of the mechanism outer box 1 is equipped with calculating master Plate 4, for exporting real-time recognition result in collecting the data for recording and handling monocular high definition RGB camera 3 and laser radar 2;Institute It states monocular high definition RGB camera 3 and image is generated with the rate of 30Hz, and compressed with 1920 × 1080 resolution ratio;It is described to swash Optical radar 2 generates data with the frequency of 10Hz, with 360 degree of full filed;The capture range of the laser radar 2 is 0.9- 130m;The data for calculating mainboard 4 and receiving monocular high definition RGB camera 3 and laser radar 2 with 2,000,000,000 bytes/minute.
The real-time identification mechanism of a kind of objective based on Multi-sensor Fusion of the present embodiment, using multiple RGB cameras And laser radar apparatus, realize the real-time identification function of the target of Multi-sensor Fusion.
As shown in figure 4, the present embodiment additionally provides a kind of objective real-time identification method based on Multi-sensor Fusion, What it was specifically carried out according to the following steps:
One, laser radar, monocular high definition RGB camera are connect with mainboard is calculated, configures laser radar and camera calibration text Part;
Two, it is analyzed by the data of laser radar, by the PCD of input, excludes ground point cloud data, and based on equal The PCD of value drift is detected and positioning, exports the three-dimensional bezel locations information and tracking information of object;
Three, based on the recognition result of step 2, processing point is carried out to the two-dimensional image data result of monocular high definition RGB camera Analysis, and the detection of the RGB image based on average drifting and positioning, the two-dimentional bezel locations information and tracking letter of output identification object Breath;
Four, the two-dimensional signal of step 3 is mapped in the three-dimensional information of step 2, with Kalman filtering by two dimension three Information fusion is tieed up, the real-time identification information of object is robustly obtained with acceleration models such as three-dimensional Kalmans, that is, completes to know in real time Not.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment, in step 2, exclusively Face point cloud data removes ground point cloud information with Density Estimator.By the equal acceleration models in conjunction with Kalman filtering come The robustness for increasing removal ground point figure information, defines angle searching threshold value by engineering experience to reduce the influence of outlier, Point cloud data after processed detects track algorithm by average drifting, output identify object three-dimensional bezel locations information and with Track information.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment in step 3, will obtain Three-dimensional frame information MAP two-dimensional convex closure information is obtained into two dimensional image.Two-dimentional convex closure amplifies by ratio, by right The calculating of number likelihood ratio, obtains object discrimination color model;In Convex range, track algorithm is detected by average drifting, it is defeated The two-dimentional bezel locations information and tracking information of object are identified out.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment, in step 3, with one The equal acceleration models in conjunction with Kalman filtering are tieed up to reduce influence of the brightness to image.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment, in step 3, based on equal The RGB image detection and positioning of value drift, object detection and positioning the reflecting from present frame of the next frame based on average drifting The mass center penetrated starts.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment, in step 4, Kalman Filtering is for merging and tracking the object mass center obtained from image and PCD, using the Fusion Model by Kalman filtering to based on The mass center of calculation is integrated, using acceleration models such as three-dimensional Kalmans for steadily tracking and merging mass center.
A kind of objective real-time identification method based on Multi-sensor Fusion of the present embodiment is by handling and merging two Image, three dimensional point cloud are tieed up, obtains the path based on target object in identifying system coordinate system, position, speed, direction in real time As a result information can be visualized in the form of polygon outline border.
A kind of real-time identification mechanism of objective based on Multi-sensor Fusion of the present embodiment and recognition methods, based on driving The RGB camera and laser radar apparatus for sailing vehicle realize the real-time identification function of the target of Multi-sensor Fusion;By algorithm and hardware In conjunction with obtaining the path based on target object in identifying system coordinate system in real time, position, speed, directional information as a result can be with The form of polygon outline border visualizes, and can quickly apply to the avoidance in unmanned field on the spot, path planning etc., and Common camera and laser radar can be carried, cost is considerably reduced.
Above-described embodiment should not in any way limit the present invention, all to be obtained by the way of equivalent replacement or equivalency transform Technical solution fall within the scope of protection of the present invention.

Claims (10)

1. the real-time identification mechanism of objective based on Multi-sensor Fusion, it is characterised in that: it includes mechanism outer box;The machine Structure outer box are regular pentagon box body;Laser radar is installed at the top of the mechanism outer box, it is real-time for the objective in PCD Identification;The middle position on five sides of the mechanism outer box is separately installed with monocular high definition RGB camera, for two in RGB image Dimension target identifies in real time;The inside of the mechanism outer box is equipped with calculating mainboard, for recording in collection and handling monocular high definition The data of RGB camera and laser radar export real-time recognition result.
2. the real-time identification mechanism of the objective according to claim 1 based on Multi-sensor Fusion, it is characterised in that: institute It states monocular high definition RGB camera and image is generated with the rate of 30Hz, and compressed with 1920 × 1080 resolution ratio.
3. the real-time identification mechanism of the objective according to claim 1 based on Multi-sensor Fusion, it is characterised in that: institute It states laser radar and data is generated with the frequency of 10Hz, with 360 degree of full filed.
4. the real-time identification mechanism of the objective according to claim 1 based on Multi-sensor Fusion, it is characterised in that: institute The capture range for stating laser radar is 0.9-130m.
5. the objective real-time identification method based on Multi-sensor Fusion, it is characterised in that: be specifically to carry out according to the following steps :
One, laser radar, monocular high definition RGB camera are connect with mainboard is calculated, configures laser radar and camera calibration file;
Two, it is analyzed by the data of laser radar, by the PCD of input, excludes ground point cloud data, and float based on mean value The PCD of shifting is detected and positioning, exports the three-dimensional bezel locations information and tracking information of object;
Three, based on the recognition result of step 2, processing analysis is carried out to the two-dimensional image data result of monocular high definition RGB camera, And RGB image detection and positioning based on average drifting, the two-dimentional bezel locations information and tracking information of output identification object;
Four, the two-dimensional signal of step 3 is mapped in the three-dimensional information of step 2, believes two-dimentional three-dimensional with Kalman filtering Breath fusion, the real-time identification information of object is robustly obtained with acceleration models such as three-dimensional Kalmans, that is, completes identification in real time.
6. the objective real-time identification method according to claim 5 based on Multi-sensor Fusion, it is characterised in that: step In rapid two, ground point cloud data are excluded, ground point cloud information is removed with Density Estimator.By combining Kalman filtering Equal acceleration models increase the robustness of removal ground point figure information, angle searching threshold value is defined by engineering experience come reduce from The influence of group's value, the point cloud data after processing detect track algorithm, the three-dimensional frame of output identification object by average drifting Location information and tracking information.
7. the objective real-time identification method according to claim 5 based on Multi-sensor Fusion, it is characterised in that: step In rapid three, obtained three-dimensional frame information MAP is obtained into two-dimensional convex closure information into two dimensional image.Two-dimentional convex closure pass through than Rate amplification, by the calculating of log-likelihood ratio, obtains object discrimination color model;In Convex range, visited by average drifting Survey track algorithm, the two-dimentional bezel locations information and tracking information of output identification object.
8. the objective real-time identification method according to claim 5 based on Multi-sensor Fusion, it is characterised in that: step In rapid three, influence of the brightness to image is reduced with the equal acceleration models of one-dimensional combination Kalman filtering.
9. the objective real-time identification method according to claim 5 based on Multi-sensor Fusion, it is characterised in that: step In rapid three, the detection of RGB image based on average drifting and positioning, the object detection of the next frame based on average drifting and positioning from The mass center of mapping in present frame starts.
10. the objective real-time identification method according to claim 5 based on Multi-sensor Fusion, it is characterised in that: In step 4, Kalman filtering is for merging and tracking the object mass center obtained from image and PCD, using based on Kalman filtering Fusion Model the mass center of calculating is integrated, using the acceleration models such as three-dimensional Kalman for steadily track and merge matter The heart.
CN201810800374.XA 2018-07-20 2018-07-20 The real-time identification mechanism of objective and recognition methods based on Multi-sensor Fusion Pending CN109002800A (en)

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Application publication date: 20181214