CN113137971A - RANSAC improvement method suitable for instant positioning and map construction - Google Patents
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Abstract
The invention discloses a RANSAC (random sample consensus) improvement method suitable for instant positioning and map construction, which comprises the steps of randomly selecting n sample estimation models, and calculating all points by utilizing the sample estimation models to obtain the number of internal points; updating the optimal model according to the number of the interior points, and storing the interior points; estimating an updated optimal model by using a least square method and based on a set threshold value, and storing corresponding interior points; randomly selecting m samples from the corresponding inner points, overlapping the samples to estimate a model, and updating the model if the error is reduced until the best model appears or the highest iteration number is reached; the invention improves RANSAC by combining the least square method, and improves the precision and the robustness of the pose tracking thread of the SLAM.
Description
Technical Field
The invention relates to the technical field of computer vision, image processing and positioning navigation, in particular to a RANSAC improvement method suitable for instant positioning and map construction.
Background
The self-positioning of the mobile robot in an unknown environment and the establishment of an environment model are closely related. The positioning can be realized without leaving the environment model, and the accuracy of the environment model depends on the positioning precision. The robot has no reference object in an unknown environment, and can only acquire external information by means of a sensor which is not very accurate and is equipped by the robot, and at the moment, accurate positioning is very difficult to realize. The existing map implementation positioning and the known position creation map are easy to implement, but the positioning without the map and the map creation without the positioning have no way to do. Solutions to this type of problem in existing research can be divided into two categories: a plurality of sensors carried by the sensor, including a mileage gauge, a gyroscope, an acceleration sensor and the like, are utilized, and positioning errors are reduced through fusion of a plurality of sensing information carried by the sensor. Most of fusion algorithms using multiple sensors are based on Kalman filtering methods, and because the methods do not refer to external information, the accumulated error is large. Another method relies on an internal self-sensor to estimate self-movement, uses an external sensor such as a laser range finder, a vision sensor and the like to sense the environment, analyzes the obtained information, extracts environmental characteristics for storage, and corrects the self-position in the next step by comparing the environmental characteristics, but the method relies on the environmental characteristics.
SLAM (immediate positioning and mapping) is used to solve the problem of positioning and mapping of mobile robots in unknown environments. SLAMs can be classified into laser SLAMs and visual SLAMs according to the type of sensor. Laser SLAM is implemented using a laser radar, and visual SLAM uses visual sensors such as monocular, binocular, RGB-D phases. Laser SLAM starts earlier than visual SLAM, and the prior art tends to be mature, and the precision is high. The 2D laser can establish a two-dimensional map of an environment, the 3D laser can establish a 3D map according to collected point clouds, but the cost of the laser radar is very high, the cost of the visual sensor is relatively low, application scenes are rich, and the visual SLAM can establish a sparse map, a semi-dense map and a dense map.
The early visual SLAM utilizes SIFT characteristics, has high positioning precision, but has high time cost, and cannot realize the real-time performance of the SLAM. Bailey et al use extended kalman filtering to calculate pose, but the accumulated error is unavoidable.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a RANSAC improvement method suitable for instant positioning and map construction, which can solve the problems of low positioning accuracy and large error at a corner due to frame loss.
In order to solve the technical problems, the invention provides the following technical scheme: randomly selecting n sample estimation models, and calculating all sample points by using the sample estimation models to obtain the number of internal points; updating the optimal model according to the number of the interior points, and storing the interior points; estimating an updated optimal model by using a least square method and based on a set threshold value, and storing corresponding interior points; and randomly selecting m samples from the corresponding interior points, iterating the sample estimation model, and updating the model if the error is reduced until the best model appears or the highest iteration times is reached.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: the number of interior points includes the number of interior points,
wherein d ispIs the probability that p points are interior points.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: and if the number of the interior points exceeds the number of the interior points of the optimal model, updating the optimal model.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: the set threshold value comprises the following steps:
Z＝ck
wherein k is the threshold value of the updated optimal model, and c is a multiple.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: estimating the updated optimal model comprises estimating using a least squares method, assuming F (x, a) as the updated optimal model, defining an objective function as:
wherein M is a polynomial order, w is a polynomial coefficient, w1,w2…wMIs denoted as A.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: further comprising, defining an error function to evaluate the objective function:
where t is the estimated updated best model.
As a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: the iteration times include that if f is the probability that all points randomly selected from the data set are interior points, the iteration times s are as follows:
as a preferable solution of the RANSAC improved method suitable for instant positioning and mapping according to the present invention, wherein: further comprising that the probability f that all the randomly selected points from the data set are interior points is:
f＝1-(1-dp)s。
the invention has the beneficial effects that: the invention improves RANSAC by combining the least square method, and improves the precision and the robustness of the pose tracking thread of the SLAM.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a RANSAC improved method for instant positioning and mapping according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of unscreened mismatch in a RANSAC improved method for instant position and mapping according to a second embodiment of the present invention;
fig. 3 is a diagram illustrating a second embodiment of the present invention after a filtering mismatch of the RANSAC improved method for instant positioning and mapping;
fig. 4 is a front view of a sequence fr1_ desk of the RANSAC improved method for instant location and mapping according to the second embodiment of the present invention;
fig. 5 is a top view of a sequence fr1_ desk of the RANSAC improved method for instantaneous localization and mapping according to a second embodiment of the present invention;
fig. 6 is a front view of a sequence fr2_ xyz of the RANSAC adaptation method for instantaneous localization and mapping according to the second embodiment of the present invention;
fig. 7 is a top view of a sequence fr2_ xyz adapted to the RANSAC improved method for instantaneous localization and mapping according to the second embodiment of the present invention;
fig. 8 is a main view of a sequence fr3_ office of the RANSAC improved method for instantaneous location and mapping according to the second embodiment of the present invention;
fig. 9 is a top view of a sequence fr3_ office of the RANSAC improved method for instantaneous localization and mapping according to the second embodiment of the present invention;
fig. 10 is a sequence 05 of a RANSAC adaptation method for instantaneous position location and mapping according to a second embodiment of the present invention;
fig. 11 is a sequence 07 of a RANSAC improved method for instantaneous position location and mapping according to a second embodiment of the present invention;
fig. 12 is a diagram of a sequence V1_01_ easy of a RANSAC improved method for instantaneous location and mapping according to a second embodiment of the present invention;
fig. 13 is a diagram of a sequence V1_02_ medium of a RANSAC improved method for instantaneous location and mapping according to a second embodiment of the present invention;
fig. 14 is a diagram of a sequence V2_02_ medium of a RANSAC improved method for instantaneous location and mapping according to a second embodiment of the present invention;
fig. 15 is a diagram illustrating a sequence MH _03_ medium of a RANSAC improved method for instantaneous location and mapping according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a RANSAC improvement method suitable for instant positioning and mapping, which includes:
ransac (random Sample consensus) is an algorithm for calculating mathematical model parameters of data according to a set of Sample data sets containing abnormal data to obtain valid Sample data.
S1: and randomly selecting n sample estimation models, and calculating all points by using the sample estimation models to obtain the number of the internal points.
It should be noted that the sample estimation model may be a unitary primary model, a binary primary model, or a unitary secondary model, and the sample estimation model of this embodiment is:
y＝ax+b
and substituting all the points into the sample estimation model, wherein the points in the sample estimation model are considered as inner points, and otherwise, the points are outer points.
Assuming that p points need to be selected by the sample estimation model, the probability d that the p points are all interior pointspComprises the following steps:
s2: and updating the optimal model according to the number of the interior points, and storing the interior points.
If the number of inliers exceeds the number of inliers of the optimal model, the optimal model is updated by saving, replacing, and overwriting.
S3: and estimating the updated optimal model by using a least square method and based on a set threshold value, and storing the corresponding interior points.
According to the sample data, the least square estimation formula is adopted to obtain the estimation quantity of the parameters of the simple linear regression model.
The set threshold Z is:
Z＝ck
wherein k is the threshold value of the updated optimal model, and c is a multiple.
Estimating by using a least square method, assuming that F (x, A) is an updated optimal model, estimating by using an objective function, and defining the objective function as follows:
defining an error function based on the mean square error to evaluate an objective function:
wherein M is a polynomial order, w is a polynomial coefficient, w1,w2…wMAnd the estimated updated optimal model is marked as A and t.
S4: and randomly selecting m samples from the corresponding interior points, iterating the sample estimation model, and updating the model if the error is reduced until the best model appears or the highest iteration number is reached.
If f is the probability that all the randomly selected points in the data set are interior points, the iteration times s are as follows:
wherein, 1-dpThe probability that at least one point in the p points is an out-of-office point is set;
the probability f that all randomly selected points from the dataset are interior points is:
f＝1-(1-dp)s。
wherein (1-d)p)sThe probability that p points are local points is never selected by the expression algorithm.
Example 2
To demonstrate the technical effect of the method, the present example was conducted in a number of cases using a TUM with data sets collected by an indoor hand-held camera, KITTI with data sets collected by outdoor highway and urban traffic and EuRoC with data sets collected by a Micro Aerial Vehicle (MAV) for comparative testing.
The parameters are set as follows: the value of f is 0.99, the maximum iteration number is 300, the number of randomly selected samples is 4, the probability d of selecting an inner point from the data set each time is 0.5, and the threshold value of the maximum error is judged to be 5.991 sigma2Wherein σ is 0.3; the number of randomly selected samples during iteration is: min (14, I/2), where I is the number of inner points of the previous round, the threshold change by a factor of 0.7.
(1) From a qualitative perspective, fig. 2 and fig. 3 show the matching of two frames of fr1_ desk, fig. 2 is the result without removing the mismatch, fig. 3 is the result after removing the mismatch by the method; as can be seen from the boxes, the method greatly reduces the points of mismatch so that the matches are in the same direction.
FIGS. 4 to 9 show the comparison of the positioning effect of the present method and the RM method; we draw the actual trajectories (solid lines) of fr1_ desk, fr2_ xyz, and fr3_ office of the TUM dataset, the estimated trajectories (dotted lines) of RM, and the top and front views of the estimated trajectories (dotted lines) of the method, partially enlarged, and see that the method is closer to the actual trajectories.
FIGS. 10-11 are the 05 and 07 sequences of KITTI datasets (both closed-loop), with the solid line being the actual trajectory, the dashed line being the trajectory estimated by the method, and the dotted line being the trajectory estimated by the RM method, it can be seen that the method outperforms the RM method, especially in the corner portion;
fig. 12 to 15 show the trajectory of the EuRoC dataset, the dotted line shows the actual trajectory, and the solid line shows the trajectory estimated by the method.
(2) From a quantitative perspective, table 1 compares the root mean square error of two estimates of the TUM data set.
Table 1: root mean square error (unit: meter) of the estimated trajectory of the TUM data set
| Sequence of | fr1_desk | fr2_xyz | fr3_office | fr3_nst |
| RM method | 0.016 | 0.004 | 0.010 | 0.018 |
| Method for producing a composite material | 0.014 | 0.003 | 0.009 | 0.011 |
It can be seen that the method is more accurate in positioning, especially fr1_ desk and fr3_ nst, which are 12.5% and 38.9% lower in root mean square error than the RM method, respectively.
Table 2 compares the root mean square error of two estimates for the KITTI dataset,
table 2: root mean square error (unit: meter) of KITTI data set estimation trajectory
| Sequence of | 05 | 07 | 08 |
| RM method | 0.94 | 0.58 | 4.8 |
| Method for producing a composite material | 0.89 | 0.52 | 4.8 |
It can be seen that in the 05 and 07 sequences, the root mean square error of the method is smaller than that of the RM method, the root mean square error is respectively accurate to 5 cm and 6 cm, and the sequence 08 and the RM method achieve the same positioning error, so that the method is proved to have strong fault tolerance.
In practical application, the related parameters related to the method need to be set, and the experimental result of the embodiment proves that under the parameter setting, the method has a very good effect in the aspects of positioning accuracy, real-time performance, robustness and fault tolerance.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A RANSAC improvement method suitable for instant positioning and map construction is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
randomly selecting n sample estimation models, and calculating all sample points by using the sample estimation models to obtain the number of internal points;
updating the optimal model according to the number of the interior points, and storing the interior points;
estimating an updated optimal model by using a least square method and based on a set threshold value, and storing corresponding interior points;
and randomly selecting m samples from the corresponding interior points, iterating the sample estimation model, and updating the model if the error is reduced until the best model appears or the highest iteration times is reached.
2. A RANSAC adaptation method for instant positioning and mapping as claimed in claim 1, characterized in that: the number of interior points includes the number of interior points,
wherein d ispIs the probability that p points are interior points.
3. RANSAC adaptation method for instant positioning and mapping as claimed in claim 1 or 2, characterized in that: also comprises the following steps of (1) preparing,
and if the number of the interior points exceeds the number of the interior points of the optimal model, updating the optimal model.
4. A RANSAC adaptation method for instant positioning and mapping as claimed in claim 3, characterized in that: the set threshold values may include, for example,
the set threshold value Z is as follows:
Z＝ck
wherein k is the threshold value of the updated optimal model, and c is a multiple.
5. A RANSAC improved method for instant positioning and mapping as claimed in any of claims 1, 2, 4, characterized by: estimating the updated best model includes estimating a model of the target,
estimating by using a least square method, assuming that F (x, A) is the updated optimal model, and defining an objective function as:
wherein M is a polynomial order, w is a polynomial coefficient, w1,w2…wMIs denoted as A.
6. A RANSAC adaptation method for instant positioning and mapping as claimed in claim 5, characterized in that: also comprises the following steps of (1) preparing,
defining an error function to evaluate the objective function:
where t is the estimated updated best model.
7. A RANSAC adaptation method for instant positioning and mapping as claimed in claim 5, characterized in that: the number of iterations includes, for example,
if f is the probability that all the randomly selected points in the data set are interior points, the iteration times s are as follows:
8. a RANSAC adaptation method for instant positioning and mapping as claimed in claim 7, wherein: also comprises the following steps of (1) preparing,
the probability f that all the randomly selected points in the data set are interior points is as follows:
f＝1-(1-dp)s。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116524011A (en) * | 2023-04-26 | 2023-08-01 | 北京航空航天大学 | Refinement method of robot to target pose in home environment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3182371A1 (en) * | 2015-12-17 | 2017-06-21 | STmicroelectronics SA | Threshold determination in for example a type ransac algorithm |
| CN107437264A (en) * | 2017-08-29 | 2017-12-05 | 重庆邮电大学 | In-vehicle camera external parameter automatic detection and bearing calibration |
| CN107633518A (en) * | 2017-09-26 | 2018-01-26 | 南昌航空大学 | A kind of product design detection method based on Kinect |
| CN108921893A (en) * | 2018-04-24 | 2018-11-30 | 华南理工大学 | A kind of image cloud computing method and system based on online deep learning SLAM |
| CN109682385A (en) * | 2018-11-05 | 2019-04-26 | 天津大学 | A method of instant positioning and map structuring based on ORB feature |
| CN110163273A (en) * | 2019-05-14 | 2019-08-23 | 西安文理学院 | It is a kind of that genic image matching method is had based on RANSAC algorithm |
| CN112085845A (en) * | 2020-09-11 | 2020-12-15 | 中国人民解放军军事科学院国防科技创新研究院 | Outdoor scene rapid three-dimensional reconstruction device based on unmanned aerial vehicle image |
-
2021
- 2021-03-24 CN CN202110313984.9A patent/CN113137971A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3182371A1 (en) * | 2015-12-17 | 2017-06-21 | STmicroelectronics SA | Threshold determination in for example a type ransac algorithm |
| CN107437264A (en) * | 2017-08-29 | 2017-12-05 | 重庆邮电大学 | In-vehicle camera external parameter automatic detection and bearing calibration |
| CN107633518A (en) * | 2017-09-26 | 2018-01-26 | 南昌航空大学 | A kind of product design detection method based on Kinect |
| CN108921893A (en) * | 2018-04-24 | 2018-11-30 | 华南理工大学 | A kind of image cloud computing method and system based on online deep learning SLAM |
| CN109682385A (en) * | 2018-11-05 | 2019-04-26 | 天津大学 | A method of instant positioning and map structuring based on ORB feature |
| CN110163273A (en) * | 2019-05-14 | 2019-08-23 | 西安文理学院 | It is a kind of that genic image matching method is had based on RANSAC algorithm |
| CN112085845A (en) * | 2020-09-11 | 2020-12-15 | 中国人民解放军军事科学院国防科技创新研究院 | Outdoor scene rapid three-dimensional reconstruction device based on unmanned aerial vehicle image |
Non-Patent Citations (8)
| Title |
|---|
| 周钦坤;岳建平;杨恒;朱依民;: "机载LiDAR数据中电力线的自动提取与重建", 测绘通报, no. 10, 25 October 2020 (2020-10-25) * |
| 徐岩;安卫凤;: "基于改进随机抽样一致算法的视觉SLAM", 天津大学学报(自然科学与工程技术版), no. 10 * |
| 徐岩;安卫凤;: "基于改进随机抽样一致算法的视觉SLAM", 天津大学学报(自然科学与工程技术版), no. 10, 2 September 2020 (2020-09-02) * |
| 汤井田;王凯;肖嘉莹;: "基于SIFT特征检测的医学显微图像自动拼接", 计算机工程与应用, no. 35 * |
| 汤井田;王凯;肖嘉莹;: "基于SIFT特征检测的医学显微图像自动拼接", 计算机工程与应用, no. 35, 11 December 2007 (2007-12-11) * |
| 陈劭;郭宇翔;高天啸;宫清源;张军国;: "移动机器人RGB-D视觉SLAM算法", 农业机械学报, vol. 49, no. 10, pages 39 - 45 * |
| 魏丽芳;潘林;黄琳琳;余轮;: "基于PCA-SIFT特征检测的眼底图像拼接", 微计算机信息, no. 08 * |
| 魏丽芳;潘林;黄琳琳;余轮;: "基于PCA-SIFT特征检测的眼底图像拼接", 微计算机信息, no. 08, 15 March 2010 (2010-03-15) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116524011A (en) * | 2023-04-26 | 2023-08-01 | 北京航空航天大学 | Refinement method of robot to target pose in home environment |
| CN116524011B (en) * | 2023-04-26 | 2024-04-30 | 北京航空航天大学 | Refinement method of robot to target pose in home environment |
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