CN117570958A - Lubang positioning method applying unstructured environment - Google Patents
Lubang positioning method applying unstructured environment Download PDFInfo
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- CN117570958A CN117570958A CN202311448876.8A CN202311448876A CN117570958A CN 117570958 A CN117570958 A CN 117570958A CN 202311448876 A CN202311448876 A CN 202311448876A CN 117570958 A CN117570958 A CN 117570958A
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- G—PHYSICS
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- 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
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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Abstract
The invention discloses a robust positioning method applying a non-structural environment, which is characterized in that a navigation coordinate system and an airborne coordinate system are established, wherein the navigation coordinate system is a coordinate system taking the ground as a reference, and is used for describing the motion state of a mobile robot when the ground is taken as the reference, and the robot adopts wheel type odometer positioning, inertial measurement unit positioning, ultra-wideband positioning and laser radar positioning; the invention has the following advantages: according to the scheme, the wheel type odometer positioning, the inertial measurement unit positioning, the ultra-wideband positioning and the laser radar positioning fusion positioning are adopted for the robot aiming at the indoor environment, higher positioning precision and robustness can be obtained through fusion complementary positioning of various sensors, and an automatic partial coverage type map construction method based on self-evaluation of map quality can be used for filtering dynamic objects, describing strategies of similarity and map updating strategies, so that the positioning problem of the robot in a high dynamic environment is effectively solved.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a robust positioning method applying a non-structural environment.
Background
The unstructured environment is an environment which lacks definite rules and modes and is usually more complex, random or chaotic, and can be generally regarded as a high dynamic and high-change rate environment, so that the realization of more stable robust positioning in a certain unstructured environment is an urgent problem, and in the field of robots, in order for the robots to reach a preset target point in the environment, the robots need to know the accurate position in a prior map of the robots, and the prior map is generally obtained through a pre-mapping operation, so that prior map data acquired in the past cannot be matched with the environment which has been changed currently. In addition, the high dynamic environment change can also have a severe influence on positioning, so in order to improve the positioning robustness, the following optimization strategies are provided: 1. multisensor fusion: multiple sensors, such as lidar, cameras, GPS, inertial Measurement Units (IMUs), etc., are used to fuse their data together to improve accuracy and robustness of positioning. Sensor fusion can mitigate inaccuracy of a certain sensor under certain conditions. Aiming at some scenes with overlarge environmental changes, the positioning failure of an outer sensor based on a laser radar and the like is likely to be caused, so that when the outer sensor fails, dead reckoning can be carried out through the data fusion of an inner sensor such as an odom and an imu and the like, but the positioning Lu Bang can only be ensured in a short time, and accumulated errors exist; 2. external-based reference points: in some cases, external reference points or markers may be used to assist in the positioning of the robot. For example, a QR code or a marker is used in an indoor environment to determine the position of the robot. But this may change the external environment, which is not allowed in some scenarios. Thus limiting its use.
Disclosure of Invention
Aiming at the technical problems, the invention provides a robust positioning method applying an unstructured environment, which solves the problems.
The technical scheme adopted for solving the technical problems is as follows: a robustly positioning method using an unstructured environment, comprising the steps of:
s1: establishing a navigation coordinate system and an airborne coordinate system, wherein the navigation coordinate system is a coordinate system taking the ground as a reference and is used for describing the motion state of the mobile robot when the ground is taken as the reference system;
s2: the robot adopts wheel type odometer positioning, inertial measurement unit positioning, ultra-wideband positioning and laser radar positioning;
s3: the wheel type odometer positioning, the inertial measurement unit positioning, the ultra-wideband positioning and the laser radar positioning in the step S2 realize multi-sensor fusion positioning by a probability-based method;
s4: in step S3, preprocessing data of wheel type odometer positioning, inertial measurement unit positioning, ultra-wideband positioning and laser radar positioning based on probability, obtaining pose estimation of a mobile robot, and fusing data of a decision level to be used for positioning a target in real time;
s5: the robot system establishes a likelihood domain model of the range finder, so that the robot system quantifies the similarity between the point cloud data from a statistical angle, thereby judging whether new data are suitable for map updating, and the system can quickly and accurately judge whether the new point cloud data are matched with the existing map data through the likelihood domain model;
s6: the neural network and the intelligent map updating strategy are introduced into the robot system, the robot can detect and filter the dynamic object in real time through the neural network, so that the influence of the dynamic object on the local map construction and positioning is eliminated, the similarity between the new point cloud data and the existing map data is higher than a preset threshold value through the intelligent map updating strategy, the system only updates the current map data and maps the new data into the existing map, and the strategy is favorable for maintaining the accuracy of the map in a changed environment and reducing positioning errors caused by environmental change.
Further, in the step S5, the data fusion of the decision stage refers to the fusion of the data processed by the sensor.
Further, in the step S4, the tracking method for locating the target in real time through the data fusion of the decision stage includes a kalman filter and a monte carlo method, the linear problem can be handled through the kalman filter, and the nonlinear problem can be handled through the monte carlo method.
Further, in step S1, the azimuth angle of the on-board coordinate system with respect to the navigation coordinate system represents the azimuth angle of the on-board coordinate system with respect to the navigation coordinate system.
Further, the wheel type odometer positioning uses a photoelectric encoder, and the rotation speed of a robot motion motor is measured through the photoelectric encoder.
Further, the speed measuring method of the photoelectric encoder comprises an M/T method, and a formula for measuring the rotating speed of the motor by the M/T method is as follows:
wherein n denotes the rotation speed, and the unit is n/min; f (f) c Representing the time base frequency of the hardware; m is M 1 Representing the number of pulses measured per unit time; z represents the number of encoder lines; m is M 2 Representing the number of time-based pulses per unit
Further, the laser radar positioning can acquire point cloud information in the environment, wherein the point cloud information refers to points formed by scanning a laser beam onto an obstacle
Further, the formula of the included angle between the laser beam scanning line and the reference line is:
wherein i is the ith reflection point in a certain frame of laser scanning data; 4000 is a weekly sampling point.
Further, the wheel type odometer positioning, the inertial measurement unit positioning, the ultra-wideband positioning and the laser radar positioning are integrated and positioned, and the ultra-wideband positioning is characterized in that absolute position information is provided during positioning, attitude information is not provided, drift is small, noise of the inertial measurement unit positioning and the wheel type odometer positioning is small, high-precision positioning can be realized under an ideal laser radar positioning state, noise is small, and drift is small
Further, the inertial measurement unit positioning is to calculate the mobile machine by means of an inertial navigation algorithm, and a posture update equation of the inertial measurement unit positioning under a navigation coordinate system is as follows;
in the method, in the process of the invention,recording an attitude transformation matrix from a coordinate system to a navigation coordinate system at the moment k+1; />A posture transformation matrix for recording the coordinate system to the navigation coordinate system at the moment k is shown; />Representing an antisymmetric matrix of relative rotation between k and k+1 corresponding to the equivalent rotation vector.
The invention has the following advantages: according to the scheme, the wheel type odometer positioning, the inertial measurement unit positioning, the ultra-wideband positioning and the laser radar positioning fusion positioning are adopted for the robot aiming at the indoor environment, higher positioning precision and robustness can be obtained through fusion complementary positioning of various sensors, and an automatic partial coverage type map construction method based on self-evaluation of map quality can be used for filtering dynamic objects, describing strategies of similarity and map updating strategies, so that the positioning problem of the robot in a high dynamic environment is effectively solved.
Drawings
FIG. 1 is a schematic diagram of the multi-sensor fusion principle structure of a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a fused positioning routine in accordance with a preferred embodiment of the present invention.
Detailed Description
The following description of the technical solution in the embodiments of the present invention is clear and complete. All other embodiments, which are intended to be encompassed by the present invention by those of ordinary skill in the art, are intended to be within the scope of the present invention as they are not to be inventive, and numerous specific details are set forth in the following description to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and the scope of the invention is therefore not limited to the specific embodiments disclosed below.
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Referring to fig. 1-2 in combination, the present invention provides a robust positioning method using a non-structural environment, comprising the following steps:
s1: establishing a navigation coordinate system and an airborne coordinate system, wherein the navigation coordinate system is a coordinate system taking the ground as a reference and is used for describing the motion state of the mobile robot when the ground is taken as the reference system;
s2: the robot is positioned by a wheel type odometer, an Inertial Measurement Unit (IMU) and an Ultra Wideband (UWB) and a laser radar (Lidar);
s3: the wheel type odometer positioning, the inertial measurement unit positioning, the ultra wideband UWB positioning and the laser radar positioning in the step S2 realize multi-sensor fusion positioning by a probability-based method;
s4: in step S3, preprocessing data of wheel type odometer positioning, inertial measurement unit IMU positioning, ultra wideband UWB positioning and laser radar positioning based on probability, obtaining pose estimation of a mobile robot, and using decision-level data fusion for positioning a target in real time;
s5: the robot system establishes a likelihood domain model of the range finder, so that the robot system quantifies the similarity between the point cloud data from a statistical angle, thereby judging whether new data are suitable for map updating, and the system can quickly and accurately judge whether the new point cloud data are matched with the existing map data through the likelihood domain model;
s6: the neural network and the intelligent map updating strategy are introduced into the robot system, the robot can detect and filter the dynamic object in real time through the neural network, so that the influence of the dynamic object on the local map construction and positioning is eliminated, the similarity between the new point cloud data and the existing map data is higher than a preset threshold value through the intelligent map updating strategy, the system only updates the current map data and maps the new data into the existing map, and the strategy is favorable for maintaining the accuracy of the map in a changed environment and reducing positioning errors caused by environmental change.
In step S5, the decision-level data fusion refers to the fusion of data processed by the sensor, and the tracking method for locating the target in real time through the decision-level data fusion includes a kalman filter (EKF) and a monte carlo method (AMCL), and the linear problem can be processed through the kalman filter, and the nonlinear problem can be processed through the monte carlo method. For the defect that the Kalman filter can only deal with the nonlinear problem, the extended Kalman filter approximately linearizes the state transition probability and the measurement probability function by using a Taylor expansion formula, and then fuses the data by using a Kalman filtering mode. The extended kalman filtering has the advantage that redundant sensor information can be fused in real time, i.e. data sources provided by several different sensors can be fused.
In step S1, the azimuth angle of the on-board coordinate system relative to the navigation coordinate system represents the azimuth of the on-board coordinate system relative to the navigation coordinate system, and in space, the azimuth angle of the on-board coordinate system relative to the navigation coordinate system is called as the attitude angle of the robot, the attitude angle represents the azimuth of the on-board coordinate system relative to the navigation coordinate system, the wheel type odometer positioning uses a photoelectric encoder, the photoelectric encoder is used for measuring the rotating speed of a motor of the robot, the photoelectric encoder speed measuring method comprises an M/T method, and the measuring motor rotating speed formula comprises the M/T method:
wherein n denotes the rotation speed, and the unit is n/min; f (f) c Representing the time base frequency of the hardware; m is M 1 Representing the number of pulses measured per unit time; z represents the number of encoder lines; m is M 2 The number of clock pulses per unit time is indicated.
The laser radar positioning can acquire point cloud information in the environment, the point cloud information refers to points formed by scanning laser beams onto obstacles, and an included angle formula between laser beam scanning lines and reference lines is as follows:
wherein i is the ith reflection point in a certain frame of laser scanning data; 4000 is a weekly sampling point.
The wheel type odometer positioning, the inertia measurement unit positioning, the ultra-wideband positioning and the laser radar positioning are integrated and can be complemented, the ultra-wideband positioning is characterized in that absolute position information is provided during positioning, attitude information is not provided, drift is small, noise between the inertia measurement unit positioning and the wheel type odometer positioning is small, high-precision positioning can be achieved under an ideal laser radar positioning state, noise is small, and drift is also small.
The inertial measurement unit positioning is to calculate the mobile machine by means of an inertial navigation algorithm, and the attitude update equation of the inertial measurement unit positioning under a navigation coordinate system is as follows;
in the method, in the process of the invention,recording an attitude transformation matrix from a coordinate system to a navigation coordinate system at the moment k+1;
a posture transformation matrix for recording the coordinate system to the navigation coordinate system at the moment k is shown; />Representing an antisymmetric matrix of relative rotation between k and k+1 corresponding to the equivalent rotation vector.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but 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.
Other parts of the present invention not described in detail are all of the prior art, and are not described in detail herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The robust positioning method applying the unstructured environment is characterized by comprising the following steps of:
s1: establishing a navigation coordinate system and an airborne coordinate system, wherein the navigation coordinate system is a coordinate system taking the ground as a reference and is used for describing the motion state of the mobile robot when the ground is taken as the reference system;
s2: the robot adopts wheel type odometer positioning, inertial measurement unit positioning, ultra-wideband positioning and laser radar positioning;
s3: the wheel type odometer positioning, the inertial measurement unit positioning, the ultra wideband UWB positioning and the laser radar positioning in the step S2 realize multi-sensor fusion positioning by a probability-based method;
s4: in step S3, preprocessing data of wheel type odometer positioning, inertial measurement unit positioning, ultra-wideband positioning and laser radar positioning based on probability, obtaining pose estimation of a mobile robot, and fusing data of a decision level to be used for positioning a target in real time;
s5: the robot system establishes a likelihood domain model of the range finder, so that the robot system quantifies the similarity between the point cloud data from a statistical angle, thereby judging whether new data are suitable for map updating, and the system can quickly and accurately judge whether the new point cloud data are matched with the existing map data through the likelihood domain model;
s6: the neural network and the intelligent map updating strategy are introduced into the robot system, the robot can detect and filter the dynamic object in real time through the neural network, so that the influence of the dynamic object on the local map construction and positioning is eliminated, the similarity between the new point cloud data and the existing map data is higher than a preset threshold value through the intelligent map updating strategy, the system only updates the current map data and maps the new data into the existing map, and the strategy is favorable for maintaining the accuracy of the map in a changed environment and reducing positioning errors caused by environmental change.
2. The robust localization method using unstructured environments as claimed in claim 1, wherein in step S5, the decision-level data fusion refers to fusion of data processed by sensors.
3. The method of claim 1, wherein in step S4, the tracking method for real-time localization of the target by decision-level data fusion includes a kalman filter and a monte carlo method, and the linear problem can be handled by the kalman filter and the nonlinear problem can be handled by the monte carlo method.
4. A robotics positioning method using non-structural environments according to claim 1, wherein in step S1 the azimuth angle of the on-board coordinate system with respect to the navigation coordinate system represents the azimuth angle of the on-board coordinate system with respect to the navigation coordinate system.
5. A robotics positioning method using non-structural environment according to claim 1, wherein the wheel odometer positioning uses a photoelectric encoder by which the robot motion motor speed is measured.
6. The method for locating the robust position using the unstructured environment according to claim 5, wherein the method for measuring the speed of the photoelectric encoder comprises an M/T method, and the formula for measuring the rotating speed of the motor by the M/T method comprises the following steps:
wherein n denotes the rotation speed, and the unit is n/min; f (f) c Representing the time base frequency of the hardware; m is M 1 Representing the number of pulses measured per unit time; z represents the number of encoder lines; m is M 2 The number of clock pulses per unit time is indicated.
7. The method of claim 1, wherein the lidar location is configured to obtain point cloud information in the environment, the point cloud information being points formed by scanning a laser beam onto an obstacle.
8. The method for robust localization using non-structural environments according to claim 7, wherein the angle between the scan line and the reference line of the laser beam is expressed as:
wherein i is the ith reflection point in a certain frame of laser scanning data; 4000 is a weekly sampling point.
9. The robust positioning method using a non-structural environment according to claim 1, wherein the wheel type odometer positioning, the inertial measurement unit positioning, the ultra-wideband positioning and the laser radar positioning are integrated and positioned to complement each other, the ultra-wideband positioning is characterized in that absolute position information is provided during positioning, attitude information is not provided, drift is small, noise between the inertial measurement unit positioning and the wheel type odometer positioning is small, and the laser radar positioning can realize high-precision positioning under ideal conditions, and has small noise and small drift.
10. The method for robustly positioning using a non-structural environment according to claim 1, wherein the inertial measurement unit positioning is based on an inertial navigation algorithm to solve a mobile machine, and the attitude update equation of the inertial measurement unit positioning in a navigation coordinate system is as follows;
in the method, in the process of the invention,recording an attitude transformation matrix from a coordinate system to a navigation coordinate system at the moment k+1; />A posture transformation matrix for recording the coordinate system to the navigation coordinate system at the moment k is shown; />Representing an antisymmetric matrix of relative rotation between k and k+1 corresponding to the equivalent rotation vector.
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| CN117804448A (en) * | 2024-02-23 | 2024-04-02 | 北京理工大学 | Autonomous system positioning method, device, computer equipment and storage medium |
| CN117804448B (en) * | 2024-02-23 | 2024-04-30 | 北京理工大学 | Autonomous system positioning method, device, computer equipment and storage medium |
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