CN111121767B - GPS-fused robot vision inertial navigation combined positioning method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses a robot vision inertial navigation combined positioning method integrating GPS, which comprises the steps of extracting and matching left and right images of a binocular camera with front and rear image feature points, and calculating the relative pose of the feature point three-dimensional coordinates and an image frame; selecting a key frame in an image stream, creating a sliding window, and adding the key frame into the sliding window; calculating a visual re-projection error, and combining an IMU pre-integral residual error and a zero offset residual error into a combined pose estimation residual error; performing nonlinear optimization on the joint pose estimation residual error by using an L-M method to obtain an optimized visual inertial navigation (VIO) robot pose; if GPS data are present at the current moment, performing self-adaptive robust Kalman filtering on the GPS position data and the VIO pose estimation data to obtain the final robot pose; if the GPS data is not available, the VIO pose data is used for replacing the final pose data. The invention improves the positioning precision of the robot, reduces the calculation consumption, and adapts to the requirement of large-scale and long-time inspection.
Description
Technical Field
The invention belongs to the technical field of automatic inspection, and particularly relates to a GPS (global positioning system) -fused robot vision inertial navigation combined positioning method.
Background
For an autonomous inspection robot, pose estimation is important, and is the basis for the robot to complete the inspection task. The traditional mode has differential GPS pose estimation, satellite signals are received through two GPS antennae, the position, the pose, the speed and the like of the inspection robot are calculated, and the precision can reach the centimeter level. However, the existing high-precision differential GPS solution is high in price, and meanwhile, high and large obstacle shielding needs to be avoided, so that the method is only suitable for robot positioning in open areas. At present, a robot simultaneous composition and positioning (SLAM) technology is popular, a local map is built by fully sensing the surrounding environment through sensors such as laser or vision, and robot information matching is performed in the local map to realize positioning. The main implementation method comprises filtering and graph optimization, wherein the filtering method only considers the optimization problem of the current and nearby poses of the inspection robot, and the global optimization cannot be realized due to the large calculation amount, so that the SLAM technology based on graph optimization is currently the mainstream. However, for the long-time and large-range inspection task required by airport inspection, the SLAM technology is limited to the computing capability of the industrial personal computer, and cannot realize the high-precision composition positioning of a large scene and a long time.
Disclosure of Invention
The invention aims to provide a combined positioning method for visual inertial navigation of a robot fusing GPS.
The technical scheme for realizing the purpose of the invention is as follows: a combined positioning method of robot vision inertial navigation integrating GPS includes the following steps:
step 1, extracting and matching left and right images of a binocular camera with front and rear image feature points, and calculating the relative pose of the three-dimensional coordinates of the feature points and an image frame;
step 2, selecting a key frame in the image stream, creating a sliding window, and adding the key frame into the sliding window;
step 3, calculating a visual re-projection error, an IMU pre-integration residual error and a zero offset residual error and combining the visual re-projection error, the IMU pre-integration residual error and the zero offset residual error into a joint pose estimation residual error;
step 4, performing nonlinear optimization on the joint pose estimation residual error by using an L-M method to obtain the optimized pose of the visual inertial navigation (VIO) robot;
step 5, if GPS data exist at the current moment, performing self-adaptive robust Kalman filtering on the GPS position data and the VIO pose estimation data to obtain the final robot pose; if the GPS data is not available, the VIO pose data is used for replacing the final pose data.
Compared with the prior art, the invention has the remarkable advantages that:
1) According to the invention, the visual inertial navigation tight coupling method is used for estimating the local relative pose of the robot, so that the estimation error caused by single sensors such as binocular vision or IMU is reduced, and the accurate local relative pose of the robot can be obtained.
2) The invention uses the GPS and binocular vision inertial navigation estimated pose as filtering fusion, and because the GPS data has no accumulated error, the robot pose estimation provides global constraint, thereby avoiding the computational power consumption of parameter global optimization and being capable of carrying out wide-range scene application.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a flow chart of binocular vision estimating robot pose and space point three-dimensional coordinates.
FIG. 3 is a flow chart of key frame determination and sliding window establishment.
Fig. 4 is a schematic diagram of sliding window optimization.
Detailed Description
As shown in fig. 1, a combined positioning method of inertial navigation of a robot vision integrating with a GPS comprises the following steps:
step 1, extracting image feature points of a left camera at the current moment according to feature points of the left camera image at the previous moment, performing feature point matching, extracting feature points of a right camera image at the current moment according to the left camera image feature points at the current moment, performing feature point matching, and calculating the relative pose of the three-dimensional coordinates of the feature points and the image frames by using the matched feature points, wherein the specific steps are as shown in fig. 2:
step 1-1, extracting a Shi-Tomashi corner point of a first frame image of a left camera by using a goodfeatureToTrack () function in opencv, and tracking by using an LK optical flow method to obtain a subsequent image characteristic point of the left camera and a right camera image characteristic point at a corresponding moment;
step 1-2, judging whether feature points obtained by tracking a subsequent left camera image by an LK optical flow method are smaller than a threshold number, if yes, extracting Shi-Tomashi corner points with phase difference number from the current left camera image;
step 1-3, triangulating matched feature points of left and right camera images, and solving three-dimensional coordinates of space points corresponding to the feature points through Singular Value Decomposition (SVD);
and step 1-4, calculating the relative pose of the image frame according to the characteristic point coordinates in the left camera image and the corresponding three-dimensional coordinates, wherein the relative pose is rotation R and displacement t respectively.
Step 2, selecting a key frame in an image stream, creating a sliding window, and adding the key frame into the sliding window, as shown in fig. 3, specifically comprising the following steps:
step 2-1, if the current image is the first frame image, taking the current image as a key frame, creating a sliding window, adding the key frame into the sliding window, and carrying out step 3; otherwise, executing the step 2-2;
step 2-2, calculating the parallax of the current image and the first frame image in the sliding window by using the formula (1), if the parallax is larger than a threshold value, taking the image as a key frame, re-establishing a new sliding window, adding the key frame into the sliding window, and performing step 3; otherwise, executing the step 2-3;
wherein Paralax is Parallax between two images, n is the number of matching feature points between two frames of images, and u 1i 、v 1i The pixel value of the ith feature point of the previous frame image.
Step 2-3, calculating the feature point number N obtained by tracking the current image from the previous frame image track And feature points N which can be traced by 4 continuous key frames in current image longtrack If equation (2) is satisfied, the sliding window can be added as a key frame, otherwise, the sliding window is discarded:
wherein N is the number of the image feature points.
Step 3, calculating a visual re-projection error, an IMU pre-integration residual error and a zero bias residual error, and combining the calculated residual error and the IMU pre-integration residual error and the zero bias residual error into a combined pose estimation residual error, wherein the method specifically comprises the following steps:
obtaining a pre-integral residual rotation residual of the IMU by using an IMU pre-integral formula according to IMU observation values among key frames
Speed residual->
Position residual->
And IMU zero bias residual gyroscope residual +.>
Accelerometer residual->
And (3) re-projecting the space point coordinates calculated in the steps (1-3) to a projection plane by using the relative pose of the image frames obtained in the steps (1-4) and using a pinhole camera model to obtain a visual re-projection error formula as follows
Wherein z is k,i Is the coordinate of the feature point corresponding to the ith space point in the kth frame image, h (·) is the pinhole camera projection formula, X k Projection matrix for the k-th frame image, P i Is the i-th spatial point coordinate.
The invention designs a joint pose estimation residual error and approximates delta x as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
f (x) is the residual quantity, N is the sliding window inner switchThe number of key frames, M, the number of three-dimensional space points of the first key frame in the sliding window.
Step 4, performing nonlinear optimization on the joint pose estimation residual error by using an L-M method to obtain the optimized pose of the visual inertial navigation (VIO) robot, wherein the method specifically comprises the following steps:
setting a trust zone matrix D, restraining the value range of Deltax, and performing first-order expansion on the formula (4) to be expressed as
Where J is the jacobian of f (x) and u is the radius of the trusted region. The constrained optimization problem is converted into an unconstrained optimization problem by utilizing Lagrangian multiplier lambda, and the constraint optimization problem is represented by the following formula:
expansion (6) and let the derivative equal to zero, it is possible to obtain:
Δx＝-(H+λI) -1 Jf(x) (7)
wherein h=j T J, I is the identity matrix. And (3) determining the iteration times, continuously adding the deltax obtained in the calculation formula (7) into x, and then re-calculating the calculation formula (7) until the iteration times are reached or deltax is smaller than a set threshold value, and obtaining the three-dimensional coordinates of the relative pose and the space point of each image in the optimized sliding window.
Step 5, if GPS data are present at the current moment, performing self-adaptive robust Kalman filtering on the GPS position data and the position and pose data of the visual inertial navigation (VIO) robot to obtain the final position and pose of the robot; if the GPS data is not available, the VIO pose data is used for replacing the final pose data, and the steps are as follows.
Step 5-1, judging whether GPS data exist, if so, turning to step 5-2; otherwise, turning to the step 5-3;
step 5-2, fusing the robot pose data of the GPS and visual inertial navigation (VIO) system, namely estimating the observation error by using adaptive robust Kalman filtering, wherein the specific steps are as follows:
step 5-2-1, predicting a state prediction equation and a covariance matrix of the system according to the state equation and the prediction equation of the system:
the system state equation and the prediction equation in the invention are as follows:
x k ＝Ax k-1 +w k-1 (10)
z k ＝Hx k +v k (11)
wherein x is k ＝[x k y k z k v x v y v z ]The speed and GPS position data output by the VIO visual inertial navigation odometer are used as observations.
For state transition matrix, I 3×3 Is a 3 x 3 unit array. H is a unit prediction matrix, and system noise w k-1 And measuring noise v k The covariance matrices are considered as gaussian white noise, Q and R, respectively.
Step 5-2-2, judging the information statistic by using the method (12)
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is an innovation matrix. When statistics T k When the threshold value is smaller than the threshold value, executing the step 5-2-3; when T is k When the current state of the robot is greater than the threshold value, the system is abnormal, the state estimation flow of the robot at the current moment is canceled, and the process goes to the step 5-3.
Step 5-2-3, designing a statistical feature updating formula of the measurement noise as follows:
wherein d k ＝(1-b)/(1-b k+1 ) B is a forgetting factor, and is generally 0.95-0.99. The kalman gain matrix is:
the state estimation and covariance matrix of the inspection robot at the current moment are respectively as follows:
P k ＝(I-K k H k )P k,k-1 (16)
and 5-3, using the current visual inertial navigation (VIO) robot pose data as a final robot pose.
Claims (4)
1. A combined positioning method of the visual inertial navigation of a robot fused with a GPS is characterized by comprising the following specific steps:
step 1, extracting characteristic points of a left camera and a right camera at the current moment, performing characteristic point matching, and calculating the relative pose of the three-dimensional coordinates of the characteristic points and an image frame by using the matched characteristic points, wherein the specific steps are as follows:
step 1-1, extracting corner points of a first frame image of a left camera, and tracking by using an LK optical flow method to obtain subsequent image characteristic points of the left camera and right camera image characteristic points at corresponding moments;
step 1-2, judging whether the number of feature points obtained from the subsequent left camera images is smaller than the threshold number, if so, extracting angular points with different numbers from the current left camera image;
step 1-3, triangulating the matched feature points of the left and right camera images, and solving the three-dimensional coordinates of the space points corresponding to the feature points through singular value decomposition;
step 1-4, calculating the relative pose of the image frame according to the feature point coordinates in the left camera image and the corresponding three-dimensional coordinates, wherein the relative pose is rotation R and displacement t respectively;
step 2, selecting a key frame in an image stream, creating a sliding window, adding the key frame into the sliding window, and selecting the key frame in the image stream specifically comprises the following steps:
step 2-1, if the current image is the first frame image, taking the current image as a key frame, creating a sliding window, adding the key frame into the sliding window, and carrying out step 3; otherwise, executing the step 2-2;
step 2-2, calculating the parallax of the current image and the first frame image in the sliding window, if the parallax is larger than a threshold value, taking the image as a key frame, re-establishing a new sliding window, adding the key frame into the sliding window, and performing step 3; otherwise, executing the step 2-3;
the calculation formula of the parallax between the current image and the first frame image in the sliding window is as follows:
wherein Paralax is Parallax between two images, n is the number of matching feature points between two frames of images, and u 1i 、v 1i The pixel value of the ith feature point of the previous frame image is the pixel value of the ith feature point of the previous frame image;
step 2-3, calculating the feature point number N obtained by tracking the current image from the previous frame image track And feature points N which can be traced by 4 continuous key frames in current image longtrack If the following formula is satisfied, the sliding window can be added as a key frame, otherwise, the sliding window is abandoned:
wherein N is the number of the image feature points;
step 3, calculating a visual re-projection error, an IMU pre-integration residual error and a zero offset residual error and combining the two residual errors into a combined pose estimation residual error;
and (3) re-projecting the space point coordinates to a projection plane by using a pinhole camera model according to the relative pose of the image frame to obtain a vision re-projection error formula as follows:
wherein z is k,i Is the coordinate of the feature point corresponding to the ith space point in the kth frame image, h (·) is the pinhole camera projection formula, X k Projection matrix for the k-th frame image, P i The i-th space point coordinate;
the joint pose estimation residual is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
f (x) is the residual quantity, N is the number of key frames in the sliding window, and the number of three-dimensional space points of the first key frame in the sliding window M;
and 4, performing nonlinear optimization on the joint pose estimation residual error by using an L-M method to obtain the optimized pose of the visual inertial navigation robot, wherein the specific method comprises the following steps:
setting a trust zone matrix D, restraining the value range of Deltax, and performing first-order expansion on the joint pose estimation residual errors, wherein the first-order expansion is expressed as:
wherein J is a jacobian matrix of f (x), u is a trust zone radius;
the constrained optimization problem is converted into an unconstrained optimization problem by utilizing Lagrangian multiplier lambda, and the constraint optimization problem is represented by the following formula:
expanding the above equation and letting the derivative equal to zero, we can obtain:
Δx＝-(H+λI) -1 Jf(x)
wherein h=j T J, I is an identity matrix;
adding delta x into x, and then re-calculating delta x until the iteration times are reached or delta x is smaller than a set threshold value, so as to obtain the optimized pose of the visual inertial navigation robot;
step 5, if GPS data exist at the current moment, performing self-adaptive robust Kalman filtering on the GPS position data and the VIO pose estimation data to obtain the final robot pose; if the GPS data is not available, the VIO pose data is used for replacing the final pose data.
2. The combined positioning method of the visual inertial navigation of the robot fused with the GPS according to claim 1, wherein the specific method for obtaining the final pose of the robot by performing adaptive robust Kalman filtering on GPS position data and visual inertial navigation (VIO) robot pose data is as follows:
the state prediction equation and covariance matrix prediction of the system are obtained according to the state equation and the prediction equation of the system, and are:
calculate the statistics of the new information, when statistics T k When the threshold value is smaller than the threshold value, executing the next step; when T is k When the position and orientation data is larger than a threshold value, the system is abnormal, the robot state estimation flow at the current moment is canceled, and the final position and orientation data is replaced by the VIO position and orientation data;
the statistical characteristic updating formula for determining the measurement noise is as follows:
wherein d k ＝(1-b)/(1-b k+1 ) B is a forgetting factor;
the kalman gain matrix is:
the state estimation and covariance matrix of the inspection robot at the current moment are respectively:
P k ＝(I-K k H k )P k,k-1 。
3. the combined positioning method of the robot vision inertial navigation fused with the GPS according to claim 2, wherein the system state equation and the prediction equation are as follows:
x k ＝Ax k-1 +w k-1
z k ＝Hx k +v k
wherein x is k ＝[x k y k z k v x v y v z ]The speed and GPS position data output by the VIO visual inertial navigation odometer are used as observations.
4. The combined positioning method of the integrated GPS robot vision inertial navigation according to claim 2, wherein the information statistics are
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is an innovation matrix.
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| CN110296702A (en) * | 2019-07-30 | 2019-10-01 | 清华大学 | Visual sensor and the tightly coupled position and orientation estimation method of inertial navigation and device |
| CN110472585B (en) * | 2019-08-16 | 2020-08-04 | 中南大学 | VI-S L AM closed-loop detection method based on inertial navigation attitude track information assistance |
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