CN112461228B - IMU and vision-based secondary loop detection positioning method in similar environment - Google Patents
IMU and vision-based secondary loop detection positioning method in similar environment Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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Abstract
The invention discloses a secondary loop detection positioning method based on IMU and vision in a similar environment, which comprises the following steps: step one, calibrating and synchronizing parameters of a binocular camera and an IMU; step two, extracting image features and matching the image features; thirdly, pose estimation and movement track formation; step four, loop detection; step five, a secondary loop detection mechanism; and step six, repositioning. The invention can carry out rough comparison constraint on the pose of the current image frame through the IMU pose information, namely, the current position direction of the IMU is compared with the position direction of the primary closed-loop image, and the direction consistency constraint pre-judgment is carried out, so that the problem that the surrounding scenes are similar, the current position of the IMU cannot be the same position and the surrounding scenes are wrong according to the image similarity and the image similarity can be judged to be closed-loop is prevented; the orientation error caused by similar images in similar environments is prevented through the direction prejudgment of the IMU; and after repositioning, updating the high-precision pose for correcting the accumulated drift error of the IMU, and improving the robustness of the secondary loop detection positioning method.
Description
Technical Field
The invention relates to a secondary loop detection positioning method, in particular to a secondary loop detection positioning method based on IMU and vision in a similar environment.
Background
Unmanned aerial vehicles are developing to intelligent unmanned aerial vehicles and intelligent systems, and the intelligent level of the unmanned aerial vehicles is determined by the autonomous positioning navigation capability. Unmanned aerial vehicles all put forward urgent demands on autonomous positioning capability in the fields of military reconnaissance, electric power inspection, geological exploration, forest fire prevention and the like, and particularly, along with the occurrence of satellite navigation signal interference and decoy technology, the unmanned aerial vehicle is required to have complete autonomous positioning navigation capability in a large scene so as to get rid of dependence on satellite navigation signals. The combined SLAM (Simultaneous LocalizationAnd Mapping, positioning and mapping) of vision and inertia (Inertial measurement unit, IMU for short) provides a new technical support and effective approach for realizing autonomous positioning navigation without GPS signals. Through the complementation of two different sensors, the IMU performs error correction compensation on the visual positioning information, the real-time positioning capability of the unmanned aerial vehicle is improved, but accumulated drift errors exist after the IMU runs for a long time along with the time, and if no effective means are adopted, the unmanned aerial vehicle still can be positioned to lose efficacy after a long time. Meanwhile, in the vision and IMU combined positioning method, the existing back-end loop detection positioning method optimizes the pose of the previous related image frame by judging whether the current position is the previously accessed environment area or not, and the problem of misjudgment exists, because a large number of similar environments exist in the actual environment, such as clustered buildings with similar appearance, undulating sand dunes and dense forest environments, the difficult judgment of loop detection caused by the large number of similar images in the environment without GPS is caused, so that the actual loop is caused, the algorithm judges whether the loop is not the loop or not the loop, and the misjudgment problem of loop is caused by the algorithm judgment, so that the loop is wrong and the positioning is failed. Therefore, in a similar environment without GPS, a critical issue to be solved is how to prevent erroneous loop-back due to a large number of similar environment pictures, resulting in positioning failure.
Disclosure of Invention
The invention aims to solve the technical problem of providing a secondary loop detection positioning method based on an IMU and vision in a similar environment, which can achieve the purposes of correcting vision accumulated errors and IMU accumulated drift errors and correctly positioning the loop in a similar environment without GPS.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the secondary loop detection positioning method based on IMU and vision in similar environment comprises the following steps:
step one, calibrating and synchronizing parameters of a binocular camera and an IMU:
unifying the time stamps of the binocular camera and the IMU, calibrating parameters of the binocular camera and the IMU, including the starting delay time of the binocular camera, the starting delay time of the IMU, the zero bias of the accelerometer and the zero bias of the gyroscope (zero bias, namely, when the IMU is not moved, the initial value which is not zero is output), synchronizing the time of the binocular camera and the IMU, and reading the image acquired by the binocular camera and the pose information of the IMU;
step two, image feature extraction and image matching:
detecting corner points in images acquired by a binocular camera as characteristic points, describing images around the corner points to form characteristic descriptors, matching the characteristic descriptors in two continuous images by using a Hamming distance, and screening characteristic matching point pairs to prevent mismatching;
thirdly, pose estimation and movement track formation:
calculating a rotation vector matrix R and a translation vector t which enable the square sum of errors of the feature matching point pairs to reach a minimum value by using a least square method through 3D information of the matched multiple groups of feature matching point pairs, and obtaining the pose and the moving map point track of the unmanned aerial vehicle;
when the camera moves too fast to cause image blurring and positioning information is lost, acquiring current binocular camera pose information through integrating IMU pose information under a unified timestamp, and updating associated map points;
step four, loop detection:
judging whether to return to the passing position according to the similarity of the two images so as to determine a loop detection relation, and measuring the similarity between image frames in a mode of constructing a tree cluster database so as to judge whether the positions are consistent so as to loop; clustering the feature points and feature descriptions extracted during image matching to form a crotch structure, so as to rapidly screen image features, output candidate images and reduce similarity judging time; calculating the similarity of class vectors of non-connected but adjacent key frames with common feature classes of the current image, and judging whether to loop;
step five, a secondary loop detection mechanism:
after the closed loop is confirmed in the steps, the closed loop is not firstly carried out, but the pose of the current image frame is roughly compared and restrained through IMU pose information, namely, the current position direction of the IMU is compared with the position direction of the primary closed loop image, and the direction consistency constraint pre-judgment is carried out, so that the correctness of the loop is confirmed again, and a secondary detection mechanism is formed;
step six, repositioning:
through the error result after loop confirmation, global nonlinear optimization is adopted, loop errors are evenly distributed to all key image frames, so that pose of all key image frames under a world coordinate system and associated map points are optimized and updated, and repositioning is obtained; and meanwhile, differentiating the updated current map point to correct the accumulated drift error of the current IMU.
Preferably, the calculation formula of the least square method in the third step is as follows:
wherein R is a rotation vector matrix, t is a translation vector matrix, and p i As a feature point in the current image,
for the last picture and p i Matching the corresponding feature points.
Preferably, the calculation formula of the similarity of the class vectors in the fourth step is as follows:
wherein v is 1 、v 2 Is a feature class vector of two images.
The invention has the beneficial effects that:
1. the invention has the advantages that the acquisition frequency of the adopted visual image is low, the acquisition frequency of the IMU is high, after the time stamping is synchronized, when the IMU does not have high accumulated drift error in the initial stage, the IMU has more accurate positioning information than the visual image, and the IMU information can be used for correcting the visual accumulated error;
2. according to the invention, through the combined IMU, when the camera moves too fast to cause image blurring and positioning information is lost, the current pose information is acquired through integrating the IMU pose information under the synchronous timestamp, and the associated map points are updated, so that the problem of positioning failure caused by visual image failure is solved;
3. according to the secondary loop detection positioning method provided by the invention, rough comparison constraint is carried out on the current image frame pose through IMU pose information, namely, the current position direction of the IMU is compared with the position direction of the primary closed loop image, and direction consistency constraint pre-judgment is carried out, so that the situation that surrounding scenes are similar, such as similarity between buildings, is prevented, the situation can be judged to be closed loop according to the image similarity, and the problem that the loop is mistakenly returned because the current actual position of the IMU cannot be the same position at all is solved, namely, the positioning error caused by similar images in similar environments is prevented through the direction pre-judgment of the IMU;
4. the high-precision pose updated after repositioning is used for correcting the accumulated drift error of the IMU, and the robustness of the secondary loop detection positioning method based on the IMU and vision is improved.
Drawings
Fig. 1 is a flow chart of a secondary loop detection positioning method based on IMU and vision in a similar environment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention is provided: the secondary loop detection positioning method based on IMU and vision in similar environment comprises the following steps:
step one, calibrating and synchronizing parameters of a binocular camera and an IMU:
unifying the time stamps of the binocular camera and the IMU, calibrating parameters of the binocular camera and the IMU, including binocular camera start delay time, IMU start delay time, accelerometer zero bias and gyroscope zero bias (zero bias, i.e. when the IMU is not moving, an initial value which is not zero is output), synchronizing the binocular camera and the IMU time, and reading binocular camera image and IMU pose information;
step two, image feature extraction and image matching:
detecting corner points in the images as characteristic points, describing images around the corner points to form characteristic descriptors, matching the characteristic descriptors in two continuous images by using a Hamming distance, and screening characteristic matching point pairs to prevent mismatching;
thirdly, pose estimation and movement track formation:
calculating a rotation vector matrix R and a translation vector t which enable the square sum of errors of the feature matching point pairs to reach a minimum value by using a least square method through 3D information of the matched multiple groups of feature matching point pairs, and obtaining the pose of the binocular camera and the locus of the moving map point;
when the camera moves too fast to cause image blurring and positioning information is lost, acquiring the current pose information of the double-sided camera through integrating the IMU pose information under the unified timestamp, and updating the associated map points;
step four, loop detection:
judging whether to return to the passing position according to the similarity of the two images so as to determine a loop detection relation, and measuring the similarity between image frames in a mode of constructing a tree cluster database so as to judge whether the positions are consistent so as to loop; clustering the feature points and feature descriptions extracted during image matching to form a crotch structure, so as to rapidly screen image features, output candidate images, reduce similarity judging time, calculate similarity of class vectors of non-connected but adjacent key frames with common feature classes of the current image, and judge whether loop-back;
step five, a secondary loop detection mechanism:
after the closed loop is confirmed in the steps, the closed loop is not firstly carried out, but the pose of the current image frame is roughly compared and restrained through IMU pose information, namely, the current position direction of the IMU is compared with the position direction of the primary closed loop image, and the direction consistency constraint pre-judgment is carried out, so that the correctness of the loop is confirmed again, and a secondary detection mechanism is formed;
step six, repositioning:
through the error result after loop confirmation, global nonlinear optimization is adopted, loop errors are evenly distributed to all key image frames, so that pose of all key image frames under a world coordinate system and associated map points are optimized and updated, and repositioning is obtained; and meanwhile, differentiating the updated current map point to correct the accumulated drift error of the current IMU.
In this embodiment, the calculation formula of the least square method in the third step is:
wherein R is a rotation vector matrix, t is a translation vector matrix, and p i As a feature point in the current image,
for the last picture and p i Matching the corresponding feature points.
In this embodiment, the calculation formula of the similarity of the class vectors in the fourth step is:
wherein v is 1 、v 2 Feature class for two imagesVector.
In summary, the embodiment of the invention provides a secondary loop detection positioning method based on IMU and vision in a similar environment, which performs rough comparison constraint on the pose of a current image frame through IMU pose information, namely performs direction consistency constraint pre-judgment by comparing the current position direction of the IMU with the position direction of a primary closed loop image, prevents surrounding scene similarity such as similarity between buildings, can judge as a closed loop according to the image similarity, and prevents positioning errors caused by similar images in similar environments through direction pre-judgment of the IMU according to the problem that the current actual position of the IMU cannot be the same position at all and loop errors occur; the updated high-precision pose after repositioning is used for correcting the accumulated drift error of the IMU, and the robustness of the secondary loop detection positioning method based on the IMU and vision is improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. The secondary loop detection positioning method based on IMU and vision in similar environment is characterized by comprising the following steps:
step one, calibrating and synchronizing parameters of a binocular camera and an IMU:
unifying time stamps of the binocular camera and the IMU, calibrating parameters of the binocular camera and the IMU, including binocular camera start delay time, IMU start delay time, accelerometer zero bias and gyroscope zero bias, synchronizing the binocular camera and the IMU time, and reading images acquired by the binocular camera and IMU pose information;
step two, image feature extraction and image matching:
detecting corner points in images acquired by a binocular camera as characteristic points, describing images around the corner points to form characteristic descriptors, matching the characteristic descriptors in two continuous images by using a Hamming distance, and screening characteristic matching point pairs to prevent mismatching;
thirdly, pose estimation and movement track formation:
calculating a rotation vector matrix R and a translation vector t which enable the square sum of errors of the feature matching point pairs to reach a minimum value by using a least square method through 3D information of the matched multiple groups of feature matching point pairs, and obtaining the pose of the binocular camera and the locus of the moving map point;
when the camera moves too fast to cause image blurring and positioning information is lost, acquiring pose information of the current binocular camera through integrating IMU pose information under a unified timestamp, and updating associated map points;
step four, loop detection:
judging whether to return to the passing position according to the similarity of the two images so as to determine a loop detection relation, and measuring the similarity between image frames in a mode of constructing a tree cluster database so as to judge whether the positions are consistent so as to loop; clustering the feature points and feature descriptions extracted during image matching to form a crotch structure, so as to rapidly screen image features, output candidate images and reduce similarity judging time; calculating the similarity of class vectors of non-connected but adjacent key frames with common feature classes of the current image, and judging whether to loop;
step five, a secondary loop detection mechanism:
after the closed loop is confirmed in the steps, the closed loop is not firstly carried out, but the pose of the current image frame is roughly compared and restrained through IMU pose information, namely, the current position direction of the IMU is compared with the position direction of the primary closed loop image, and the direction consistency constraint pre-judgment is carried out, so that the correctness of the loop is confirmed again, and a secondary detection mechanism is formed;
step six, repositioning:
through the error result after loop confirmation, global nonlinear optimization is adopted, loop errors are evenly distributed to all key image frames, so that pose of all key image frames under a world coordinate system and associated map points are optimized and updated, and repositioning is obtained; and meanwhile, differentiating the updated current map point to correct the accumulated drift error of the current IMU.
2. The method for detecting and positioning secondary loop based on IMU and vision according to claim 1, wherein the condition of selecting the feature matching point pair in the second step is that the hamming distance of the descriptor is less than twice the minimum distance.
3. The method for detecting and positioning the secondary loop based on the IMU and the vision in the similar environment according to claim 1, wherein the calculation formula of the least squares method in the third step is:
wherein R is a rotation vector matrix, t is a translation vector matrix, and p i As a feature point in the current image,
for the last picture and p i Matching the corresponding feature points.
4. The method according to claim 1, wherein the formula for calculating the similarity of the class vectors in the fourth step is:
wherein v is 1 、v 2 Is a feature class vector of two images.
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