CN115291241A - SLAM-based alpha/beta radiation map construction method for radiation factory - Google Patents
SLAM-based alpha/beta radiation map construction method for radiation factory Download PDFInfo
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- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
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Abstract
The invention belongs to the technical field of autonomous navigation and positioning of mobile vehicles, and particularly relates to an alpha/beta radiation map construction method for a radiation factory based on SLAM. Comprising the following steps, S100: in the moving process of the robot, environmental laser data are obtained through a laser radar, and radiation data of a scene are obtained through a surface pollution monitor; s200: processing the environmental laser data obtained in the step S100 to obtain a grid map about the nuclear radiation factory; s300: processing the radiation data obtained in the step S100, and mapping alpha/beta radiation information detected by the surface pollution monitor into a cost map so as to fuse the grid map and the radiation map; s400: and dividing the radiation intensity, setting different radiation levels and displaying the radiation levels in the finally obtained fusion result. The invention adopts SLAM technology to realize autonomous alpha/beta radiation detection of the robot.
Description
Technical Field
The invention belongs to the technical field of autonomous navigation and positioning of mobile vehicles, and particularly relates to an alpha/beta radiation map construction method for a radiation factory based on SLAM.
Background
The threat of nuclear fuel leakage is also increasing today with the widespread use of nuclear energy instead of traditional energy sources. The nuclear radiation has strong radiation damage to human bodies and also causes irreversible pollution to the environment. The robot is used for replacing manpower to carry out nuclear radiation detection, and the method has important significance for safe production and life. Is necessary for the detection of alpha rays and beta rays. Firstly, the position of a radiation source can be determined by detecting alpha rays and beta rays, so that the interference on tiny components is avoided; secondly, the radiation source is effectively detected, so that the food containing the radiation source can be prevented from being swallowed, and the aim of protecting the health and safety of a human body is fulfilled.
The current detection for alpha and beta rays is classified into the following: direct detection, indirect detection. The direct measurement is carried out by a monitoring instrument, and can measure fixed pollution and loose pollution. During measurement, a detector is placed above an object to be measured at an appropriate distance, and measurement and reading are recorded at predetermined time intervals. Indirect measurements are smear sampling methods to determine the level of loose contamination. For some immobile solids or surfaces with liquid stored therein, or for some surfaces where the measuring instrument is not close to the surface, direct measurement is difficult, and in this case only indirect measurement methods are used. The method of direct measurement or indirect measurement cannot realize the measurement of the overall radiation level of the area, and the measurement precision is difficult to guarantee. The autonomous radiation detection is realized by fixing a detector on a mobile robot on the basis of direct measurement and combining with SLAM technology to replace a worker with the robot in a complex environment. Autonomous nuclear radiation detection is becoming a trend. In recent years, autonomous and radiation detection by a robot carrying a nuclear radiation sensor has become a research hotspot.
Disclosure of Invention
The invention provides an alpha/beta radiation map construction method aiming at a radiation factory based on SLAM in order to realize the detection and the positioning of a radiation source.
The invention adopts the following technical scheme: an SLAM-based α/β radiation map construction method for a radiation factory, comprising the steps of, S100: in the moving process of the robot, acquiring environmental laser data through a laser radar, and acquiring radiation data of a scene through a surface pollution monitor; s200: processing the environmental laser data obtained in the step S100 to obtain a grid map about the nuclear radiation factory; s300: processing the radiation data obtained in the step S100, and mapping the alpha/beta radiation information detected by the surface pollution monitor into a cost map so as to fuse the grid map and the radiation map; s400: dividing the radiation intensity, setting different radiation levels, and displaying the radiation levels in the finally obtained fusion result.
The specific process of step S200 is that,
s201: and (3) state prediction: the pose of the particle at the current moment is updated by the motion model, gaussian sampling noise is added to the initial value, a rough state estimation is carried out, and the new pose of the particle point is obtained
。
S202: scanning and matching: based on the position and attitude points of the particles obtained by the rough estimation in S201, a current observation is found
With the existing map
Best fit pose to improve the proposed distribution based on the odometer model.
S203: the weight of each particle is calculated.
S204: resampling is performed according to the weight of each particle.
S205: according to sensor observation number for each particleAccording to
And the current pose of the robot
Each feature in the map is updated.
The process of the step S202 is that the robot moves and predicts the pose to six states of negative x, positive x, negative y, positive y, left rotation and right rotation, calculates the matching score in each state, and selects the pose corresponding to the highest score as the optimal pose; after the optimal particle pose is obtained, the particle sampling range is changed to a peak area represented by a laser radar observation model
(ii) a Wherein the content of the first and second substances,
is defined as
Searching out the position and pose point with the highest matching degree
Namely the observation interval
A probability peak region of (a); the peak region is then determined
Mean and variance of the represented gaussian distributions.
The specific process of step S204 is that,
by passing
Calculating effective particle volume, using effective sample volume
To measure the degree of degradation of the particles,
at the particle point set
In descending order according to weight
；
According to
From
Screening for effective particles
Wherein
Represents a nearby rounding function;
and using the disturbance particles to replace the degraded particles to ensure that the total amount of the particle points is kept unchanged, and specifically operating as follows:
wherein
And
respectively, the mean and the covariance,
represents a random perturbation, and
，
for the scaling weight of the disturbance, the larger the value is, the larger the disturbance is, the value is
(ii) a Degraded particles refer to particles not selected in the previous step of effective screening, when perturbed particles are used
Instead of degrading the particles so as to keep the total amount of particles constant.
Updating particle weights
And obtaining a new particle set.
The specific process of step S300 is that,
s301: initialization: constructing a radiation cost map on the basis of establishing the grid map in the step S200; after the robot is started for the first time or enters a new environment, obtaining the size and resolution of a user-defined radiation map and basic information of obstacles according to the grid map established in the step S200, wherein the radiation map is a blank cost map;
s302: in the process of detecting radiation information by the robot, when a dynamic obstacle is encountered, updating obstacle information in the cost map;
s303: and in the process of detecting the radiation information by the robot, updating the cost map according to the radiation information to construct a radiation map.
The specific process of step S302 is: the point cloud formed by radar detection in the robot is subjected to corresponding coordinate transformation, namely
Wherein, in the step (A),
and
is the coordinate information of the point in the map,
and
is the coordinate of the center point in the world coordinate system, res is the resolution of the map,
and
is the coordinate information of the initial point in the map; mapping the corresponding position relation into a map coordinate system from a world coordinate system, processing information acquired by the radar, wherein in the scanning range of the radar, the existing area of the point cloud is an obstacle area, and the nonexistent area is an idle area; and then, after coordinate transformation, the obstacle information obtained by the radar is compared with static map layer obstacle information and filtered, and then the information is mapped into a map coordinate system through a world coordinate system, so that the update of the dynamic obstacles in the map coordinate system is realized.
The specific process of step S303 is: the robot carries a surface pollution monitor to detect alpha and beta radiation information in a scene, and when alpha and beta particles enter a sensitive area of the composite scintillator, the alpha and beta particles interact with respective sensitive detection media in the composite scintillator to generate optical pulse signals with different amplitudes; the optical pulse signals are converted into electric pulse signals through a photomultiplier, after the electric pulse signals are preprocessed through a signal processing unit, the counting rates of alpha and beta signals are recorded through a single chip microcomputer system, and then data are output to ROS for map building; the radiation data transmitted into the ROS are the radiation values detected in the region, the radiation values are converted into cost values of a cost map in the ROS, the cost values are mapped into a blank cost map generated before, and finally the radiation map construction is completed.
The specific process of the step S400 is to set different radiation levels in the radiation detection process according to different radiation values corresponding to different cost values, and distinguish radiation pollution degrees according to the different levels, wherein green represents a low dose value, purple represents a medium dose value, pink represents a high dose value, and the radiation intensity is distinguished by color;
compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts SLAM technology to realize autonomous alpha/beta radiation detection of the robot.
2. Aiming at the problems of particle diversity and particle degradation caused by selective resampling of the Gmapping algorithm, the invention provides a random disturbance resampling method instead of the selective resampling algorithm.
3. The invention provides a radiation distribution map fusion method, which is used for mapping a radiation map with color codes to an environment grid map constructed by improved Gmapping, wherein the map has different radiation distribution levels and can display the radiation intensity in a scene in real time.
Drawings
FIG. 1 is a flow chart of map fusion according to the present invention;
fig. 2 shows a general flow diagram of the improved gmaping process of the present invention;
fig. 3 shows a process of radiation map fusion.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A SLAM-based alpha/beta radiation mapping method for a radiation factory comprises the following steps.
S100: in the moving process of the robot, environmental laser data are obtained through a laser radar, and radiation data of a scene are obtained through a surface pollution monitor.
S200: and processing the environmental laser data obtained in the step S100 to obtain a grid map about the nuclear radiation factory.
The specific process of step S200 is that,
s201: and (3) state prediction: the pose of the particle at the current moment is updated by the motion model, gaussian sampling noise is added to the initial value, a rough state estimation is carried out, and the new pose of the particle point is obtained
. The robot usually uses a pose to describe its position and posture in a space coordinate system, the position is the location of the robot in the space, and the posture is the orientation of the robot in the space.
S202: scanning and matching: this step is based on the pose points of the particles obtained by the rough estimation in S201 to find a current observation
With the existing map
Best fit pose to improve the proposed distribution based on the odometer model. The matching process is mainly based on the pose
Is taken as the center to
Searching in the area of the radius, and calculating the matching degree of the laser data observation value and the existing map. The matching process being a machineThe method comprises the steps that a person rotates towards a negative x state, a positive x state, a negative y state, a positive y state, a left rotation state and a right rotation state to move and predict a pose, and a matching score under each state is calculated (the specific process is that scanning matching aligns currently collected laser data and an environment map, and the calculation process comprises the steps of (1) converting a coordinate system of the laser data into a world map coordinate system, and (2) respectively processing six states, wherein when the laser point and the map value are determined to be barriers, the distance between the laser data and the map value under each state is smaller, the score is larger, and (3) the pose with the highest score is the optimal pose, and the pose corresponding to the highest score is selected to be the optimal pose. After the optimal particle pose is obtained, the particle sampling range can be changed to the observation interval represented by the laser radar observation model
. Wherein the interval of the distribution is observed
Can be defined as
Searching out the pose point with the highest matching degree
It is actually the observation interval
The probability peak region of (2). A relatively concentrated distribution can be given due to the observation model of the lidar. If the particle sampling range is changed to the peak region represented by the lidar observation model, the new particle distribution can be closer to the real distribution. The peak region is then determined
Mean and variance of the represented gaussian distributions. The specific process is that
Medium random samplingKPoints according to whichKThe points' odometer and observation model calculate the mean and variance as shown below:
wherein the content of the first and second substances,
。
s203: calculate the weight for each particle:
wherein
What is described is an observation model that is,
is a normalized coefficient; weight of
The difference between the target distribution and the proposed distribution is described.
S204: resampling is carried out according to the weight of each particle, and aiming at the problems of particle degradation and insufficient particle diversity of the Gmapping algorithm, a random disturbance resampling algorithm is adopted to replace a selective resampling algorithm.
The specific process of step S204 is to pass the formula
Calculating effective particle volume, using effective sample volume
The degradation degree of the particles is measured, and the smaller the value is, the larger the weight variance of the particles is, and the more serious the degradation degree of the particles is; at the particle point set
In descending order according to weight
Then according to
From
Screening for effective particles
Wherein
Representing a nearby rounding function.
And then, using the disturbance particles to replace the degraded particles to ensure that the total amount of the particle points is kept unchanged, and specifically operating as follows:
wherein
And
respectively, the mean and the covariance,
represents a random perturbation, and
，
for the scaling weight of the disturbance, the larger the value is, the larger the disturbance is, the value is
(ii) a Degraded particles refer to particles not selected in the previous step of effective screening, when perturbed particles are used
Replacing the degraded particles so as to keep the total amount of particles constant; then updating the particle weight
And obtaining a new particle set.
Before one
Screening N effective particles from the total number of particles N, and obtaining
Effective particle number; particles not screened in this process are degraded particles, in which case perturbing particles are used
Instead of degrading particles, the total number is kept constant.
S300: and (5) processing the radiation data obtained in the step (S100), and mapping the alpha/beta radiation information detected by the surface pollution monitor into a cost map, so that the grid map and the radiation map are fused.
S301: initialization: constructing a radiation cost map on the basis of establishing the grid map in the step S200; after the robot is started for the first time or enters a new environment, the size and the resolution of the user-defined radiation map and the basic information of the obstacles are obtained according to the grid map established in the step S200, and the radiation map is a blank cost map.
S302: in the process of detecting the radiation information by the robot, when a dynamic obstacle is met, the obstacle information is updated in the cost map.
The point cloud formed by radar detection in the robot is subjected to corresponding coordinate transformation, namely
Wherein, in the process,
and
is the coordinate information of the point in the map,
and
is the coordinate of the center point in the world coordinate system, res is the resolution of the map,
and
is the coordinate information of the initial point in the map; mapping the corresponding position relation into a map coordinate system from a world coordinate system, processing information acquired by the radar, wherein in the scanning range of the radar, the existing area of the point cloud is an obstacle area, and the nonexistent area is an idle area; then the obstacle information obtained by the radar is subjected to coordinate transformation, is compared and filtered with the static map layer obstacle information, is mapped into a map coordinate system through a world coordinate system, and dynamic obstacles are realized in the map coordinate systemUpdating the obstacle.
S303: and in the process of detecting the radiation information by the robot, updating the cost map according to the radiation information to construct a radiation map.
The robot carries a surface pollution monitor to detect alpha and beta radiation information in a scene, and when alpha and beta particles enter a sensitive area of the composite scintillator, the alpha and beta particles interact with respective sensitive detection media in the composite scintillator to generate optical pulse signals with different amplitudes; the optical pulse signals are converted into electric pulse signals through a photomultiplier, after the electric pulse signals are preprocessed through a signal processing unit, the single chip microcomputer system records counting rates of alpha and beta signals, and then data are output to ROS for map construction; the radiation data transmitted into the ROS are the radiation values detected in the region, the radiation values are converted into cost values of a cost map in the ROS, the cost values are mapped into a blank cost map generated before, and finally the radiation map construction is completed.
S400: dividing the radiation intensity, setting different radiation levels, and displaying the radiation levels in the finally obtained fusion result.
According to different cost values corresponding to different radiation values, different radiation levels are set in the radiation detection process, the radiation pollution degree is distinguished according to different levels, wherein green represents a low dosage value, purple represents a medium dosage value, pink represents a high dosage value, and the radiation intensity is distinguished through color.
The structures, proportions, sizes, and other dimensions shown in the drawings and described in the specification are for understanding and reading the present disclosure, and are not intended to limit the scope of the present disclosure, which is defined in the claims, and are not essential to the art, and any structural modifications, changes in proportions, or adjustments in size, which do not affect the efficacy and attainment of the same are intended to fall within the scope of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An SLAM-based alpha/beta radiation map construction method for a radiation factory is characterized in that: comprises the following steps of (a) carrying out,
s100: in the moving process of the robot, environmental laser data are obtained through a laser radar, and radiation data of a scene are obtained through a surface pollution monitor;
s200: processing the environmental laser data obtained in the step S100 to obtain a grid map about the nuclear radiation factory;
s300: processing the radiation data obtained in the step S100, and mapping the alpha/beta radiation information detected by the surface pollution monitor into a cost map so as to fuse the grid map and the radiation map;
s400: dividing the radiation intensity, setting different radiation levels, and displaying the radiation levels in the finally obtained fusion result.
2. The SLAM-based α/β radiation mapping method for a radiation factory as claimed in claim 1, wherein: the specific process of the step S200 is,
s201: and (3) state prediction: the pose of the particle at the current moment is updated by the motion model, and the noise of Gaussian sampling is added to the initial valuePerforming rough state estimation to obtain new pose of particle point
；
S202: scanning and matching: based on the position and attitude points of the particles obtained by the rough estimation in S201, a current observation is found
With the existing map
A best fit pose to improve an offered distribution based on the odometer model;
s203: calculating the weight of each particle;
s204: resampling according to the weight of each particle;
s205: observing data from the sensor for each particle
And the current pose of the robot
Each feature in the map is updated.
3. The SLAM-based α/β radiation mapping method for a radiation factory as claimed in claim 2, wherein: the process of the step S202 is that the robot moves and predicts the pose to six states of negative x, positive x, negative y, positive y, left rotation and right rotation, calculates the matching score in each state, and selects the pose corresponding to the highest score as the optimal pose; after the optimal particle pose is obtained, the particle sampling range is changed to a peak area represented by a laser radar observation model
(ii) a Wherein the content of the first and second substances,
is defined as
Searching out the position and pose point with the highest matching degree
Namely the observation interval
A probability peak region of (a); the peak region is then determined
Mean and variance of the represented gaussian distributions.
4. The SLAM-based α/β radiation mapping method for a radiation factory as claimed in claim 2, wherein: the specific process of step S204 is that,
by passing
Calculating effective particle volume, using effective sample volume
To measure the degree of degradation of the particles,
at the particle point set
In descending order according to weight
；
According to
From
Screening for effective particles
Wherein
Representing a nearest rounding function;
and using the disturbance particles to replace the degraded particles to ensure that the total amount of the particle points is kept unchanged, and specifically operating as follows:
wherein
And
respectively, the mean and the covariance,
represents a random perturbation, and
，
for the scaling weight of the disturbance, the larger the value is, the larger the disturbance is, the value is
(ii) a Degenerate particles are particles that have been deleted in the previous screening, when perturbing the particle is used
Replacing the degraded particles so as to keep the total amount of particles constant;
updating particle weights
And obtaining a new particle set.
5. The SLAM-based α/β radiation mapping method for radiation factories of claim 1, characterized in that: the specific process of step S300 is,
s301: initialization: constructing a radiation cost map on the basis of establishing the grid map in the step S200; after the robot is started for the first time or enters a new environment, obtaining the size and resolution of a user-defined radiation map and basic information of obstacles according to the grid map established in the step S200, wherein the radiation map is a blank cost map;
s302: in the process of detecting radiation information by the robot, when a dynamic obstacle is encountered, updating obstacle information in the cost map;
s303: and in the process of detecting the radiation information by the robot, updating the cost map according to the radiation information to construct a radiation map.
6. The SLAM-based α/β radiation mapping method for a radiation factory as claimed in claim 5, wherein: the specific process of step S302 is: the point cloud formed by radar detection in the robot is subjected to corresponding coordinate transformation, namely
Which isIn (1),
and
is the coordinate information of the point in the map,
and
is the coordinate of the center point in the world coordinate system, res is the resolution of the map,
and
is the coordinate information of the initial point in the map; mapping the corresponding position relation into a map coordinate system from a world coordinate system, processing information acquired by a radar, wherein the existing area of the point cloud is an obstacle area and the nonexistent area of the point cloud is an idle area in the scanning range of the radar; and then, after coordinate transformation, comparing and filtering the obstacle information obtained by the radar with the obstacle information of the static map layer, mapping the obstacle information of the static map layer into a map coordinate system through a world coordinate system, and realizing the updating of the dynamic obstacle in the map coordinate system.
7. The SLAM-based α/β radiation mapping method for radiation factories of claim 5, characterized in that: the specific process of step S303 is as follows: the robot carries a surface pollution monitor to detect alpha and beta radiation information in a scene, when alpha and beta particles enter a sensitive area of the composite scintillator, the alpha and beta particles interact with respective sensitive detection media in the composite scintillator to generate optical pulse signals with different amplitudes; the optical pulse signals are converted into electric pulse signals through a photomultiplier, after the electric pulse signals are preprocessed through a signal processing unit, the counting rates of alpha and beta signals are recorded through a single chip microcomputer system, and then data are output to ROS for map building; and transmitting the radiation data into the ROS to be the detected radiation value of the region, converting the radiation value into a cost value of a cost map in the ROS, mapping the cost value into a blank cost map generated before, and finally completing the construction of the radiation map.
8. The SLAM-based α/β radiation mapping method for a radiation factory as claimed in claim 1, wherein: the specific process of the step S400 is to set different radiation levels in the radiation detection process according to different cost values corresponding to different radiation values, and distinguish radiation pollution degrees according to the different levels, wherein green represents a low dose value, purple represents a medium dose value, pink represents a high dose value, and the radiation intensity is distinguished by color;
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