CN109798896A - A kind of positioning of Indoor Robot with build drawing method and device - Google Patents
A kind of positioning of Indoor Robot with build drawing method and device Download PDFInfo
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Abstract
The invention discloses a kind of positioning of Indoor Robot and build drawing method and device, comprising: carry out particle sampler, obtain the pose of k moment each particle, and according to the weight of each particle of particle number mean allocation；The actual observed value for obtaining the k moment, the pose in conjunction with each particle is updated to the weight of each particle and normalized；According to updated each particle weight, number of effective particles is calculated；When judging that number of effective particles is less than the threshold value of setting, particle is carried out resampling and calculated to update resampling particle weight, former sampling particle and its weight are kept when being otherwise greater than；According to resampling after update or former sampling particle weight, the attained pose of the robot at k moment is updated using the desired value of current state；The mean value and variance of map are updated using Kalman filtering algorithm, and to obtain the cartographic information of all particles, robot map is established according to attained pose.The present invention can substantially increase the real-time for building figure while guaranteeing the accuracy of map.
Description
Technical Field
The invention relates to a method and a device for positioning and mapping an indoor robot, and belongs to the technical field of mobile robots.
Background
The indoor autonomous mobile robot has a wide application prospect, and the autonomous mobile robot needs to have the capabilities of environment mapping, autonomous positioning, path planning and the like at the same time, and the positioning and mapping functions of the robot are core problems and are research hotspots in the field of the current robot.
The SLAM based on the laser radar mode still occupies an important position for positioning and navigation of the robot due to technical advantages of stability, reliability, high performance and the like. The prior method for solving the SLAM problem mainly takes probability estimation as a main part and mainly comprises a Kalman filtering algorithm and a particle filtering algorithm, the algorithm based on the particle filtering mode is not limited by strong linearity and strong Gaussian noise, more applications are obtained, the most widely used particle filtering algorithm is the RBPF algorithm at present, the method decomposes the SLAM problem into two problems of robot self-positioning and environment estimation, the particle filter is used for path estimation, and the Kalman filter is used for environment characteristic estimation, so that good effects can be realized.
The traditional RBPF-SLAM algorithm can realize more accurate positioning under the condition of less environmental features, and the algorithm occupies a large amount of memory space along with the increase of the number of required particles and the increase of the size of an environmental map, so that the algorithm efficiency is lower.
Disclosure of Invention
The invention aims to solve the technical problems that a traditional mapping algorithm occupies a large amount of memory space and has delay in a complex environment, and provides an indoor robot positioning and mapping method and device.
The invention specifically adopts the following technical scheme to solve the technical problems:
an indoor robot positioning and mapping method comprises the following steps:
step 1, sampling particles based on environmental information, acquiring the pose of each particle at the moment k, and distributing the weight of each particle according to the number of the particles;
step 2, taking the acquired distance and deviation angle between the robot and the target or the obstacle at the moment k as an actual observed value at the moment k, and updating and normalizing the weight of each particle according to the actual observed value at the moment k and the pose of each particle;
step 3, calculating to obtain the number of effective particles according to the weight of each particle updated and normalized in the step 2; when the number of the effective particles is judged to be smaller than a set threshold value, resampling the particles, and calculating and updating the weight of the resampled particles by combining the actual observed value at the moment k and the pose of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept;
step 4, according to the re-sampling particle weight or the original sampling particle weight obtained in the step 3, calculating an expected value of the current state by using a weighting formula to update the actual pose of the robot at the moment k;
step 5, updating the mean value and the variance of each particle map by using a Kalman filtering algorithm according to the pose of each particle at the moment k and the actual observation value at the moment k acquired in the step 1 to obtain map information of all particles; and establishing an actual map of the robot by combining the actual pose of the robot at the moment k updated in the step 4 with the actual observation value at the moment k and the map information of the particles corresponding to the actual pose of the robot.
Further, as a preferred technical solution of the present invention, the step 1 further includes determining whether the time k is an initial time, if so, generating a particle pose at the initial time, otherwise, obtaining an estimated pose of each particle at the time k according to a pose of a particle in a previous generation of particle set in combination with the motion model.
Further, as a preferred technical solution of the present invention, in the step 2, the weight of each particle is updated, and a formula is adopted:
wherein,representing the updated weight of the ith particle at the k moment;representing the weight of the ith particle at the moment of k-1; p represents a probability density function at time k;an actual observed value representing the ith particle at time k;indicating the pose of the ith particle at time k.
Further, as a preferred technical solution of the present invention, the step 5 of updating map information of all particles specifically includes:
sorting the particles according to the weight of the resampled particles obtained by updating or the weight of the original sampled particles, judging whether a particle set formed by the updated particles is empty or not, if so, ending the updating, otherwise, sequentially taking out the particles in the particle set;
and judging whether each particle in the extracted particle set is similar to the previous particle, namely judging whether the poses and weights of the two particles are consistent in a set range, if so, copying the weight updated by the previous particle, otherwise, updating the mean value and the variance of the particle map by using Kalman filtering again to obtain map information of all the particles.
The invention provides an indoor robot positioning and mapping device, which comprises:
the motion control module is used for acquiring the speed information of the robot and acquiring the pose of each particle at the moment k by combining the speed information of the robot;
the laser radar ranging module is used for acquiring the distance and the deviation angle between the robot and the target or the obstacle at the moment k and taking the distance and the deviation angle as the actual observed value at the moment k;
the positioning and mapping module is used for averagely distributing the weight of each particle according to the number of the particles; updating and normalizing the distributed weight of each particle according to the actual observed value at the moment k and the pose of each particle, and calculating to obtain the number of effective particles; when the number of the effective particles is judged to be smaller than the set threshold value, controlling a motion control module to resample the particles, and calculating and updating the weight of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept; according to the obtained weight of the resampling particles or the original weight of the sampling particles, calculating an expected value of the current state by using a weighting formula to update the actual pose of the robot at the moment k; updating the mean value and the variance of each particle map by using a Kalman filtering algorithm according to the acquired pose of each particle at the moment k and the actual observed value at the moment k to obtain map information of all particles; and establishing an actual map of the robot by combining the updated actual pose of the robot at the moment k with the actual observed value at the moment k and the map information of the particles corresponding to the actual pose of the robot.
By adopting the technical scheme, the invention can produce the following technical effects:
according to the indoor robot positioning and mapping method and device, resampling is carried out through a weight balance strategy, the problem of particle shortage is solved, a group of particles in a specific state are concentrated in the process of map updating, the statistical characteristics of the particles are consistent, one representative particle can be selected according to the idea of population evolution, then other particles only need to be copied, and the improved method greatly improves the real-time property of mapping while ensuring the map accuracy.
Drawings
Fig. 1 is a schematic flow chart of an indoor robot positioning and mapping method according to the present invention.
FIG. 2 is a schematic flow chart of the map updating method of the present invention.
Fig. 3 is a schematic structural diagram of an indoor robot positioning and mapping apparatus according to the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
As shown in fig. 1, the invention designs an indoor robot positioning and mapping method, which specifically comprises the following steps:
step 1, sampling particles according to the suggested distribution to obtain k timePose of each particleThe pose comprises the coordinates (x, y) and angle values of the particles and the like; and the weight of each particle is evenly distributed according to the number of the particles; the method comprises the following specific steps:
based on the environment information, particle sampling is performed, first, it is determined whether the time k is an initial time, that is, k is 0, and if yes, an initial pose vector of the particle i at the time k is 0 is generatedAnd evenly distribute the weight of the particlesWherein i is 1,2, … N, and N is the number of particles; if k is not equal to 0, the sampling is not the first sampling, and the estimated pose of each particle at the k moment is obtained according to the pose of the particles in the previous generation of particle set and the motion model as shown in FIG. 1Where the superscript i denotes the ith particle and the subscript k denotes the time of day.
Step 2, taking the acquired distance and deviation angle between the robot and the target obstacle at the moment k as an actual observed value at the moment k, and updating and normalizing the weight of each particle according to the actual observed value at the moment k and the pose of each particle; the method comprises the following specific steps:
updating the weight according to the actual observation value at the moment k, wherein different particles have respective corresponding weights, the difference between the proposed distribution and the target distribution during sampling is represented by the weight, the distance and the angle between the robot and the target at the moment k are obtained through a sensor in the laser radar ranging module to serve as the actual observation value, and the weights of the particles are updated by utilizing the following formula:
wherein,representing the updated weight of the ith particle at the k moment;representing the weight of the ith particle at the moment of k-1; p represents a probability density function at time k;an actual observed value representing the ith particle at time k;indicating the pose of the ith particle at time k.
If the weight of the particle is large, the particle is trusted to be more. After the weight value is updated, the weight value normalization processing is carried out by using the following formula:
wherein,representing the weight of the ith particle at the k moment after normalization;representing the updated weight of the ith particle at the k moment; n represents the number of particles.
Step 3, calculating to obtain the number of effective particles according to the weight of each particle updated and normalized in the step 2; when the number of the effective particles is judged to be less than the set threshold value, resampling the particles, and synchronizing the weight updating process in the step 2 to obtain the updated weight of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept; the method comprises the following specific steps:
as shown in FIG. 1, first, the effective particle number N is calculated by the following formulaeff：
Wherein,the weight of the ith particle at the time k after normalization is represented, and N represents the number of particles.
If the calculated number of significant particles NeffAnd if the sampling rate is less than the set threshold value, resampling. After the particles are iterated for several times, the weights of many particles become small, and even zero does not work, and as invalid sampling particles increase, a large amount of calculation is wasted on the particles, so that the estimation effect is relatively poor, therefore, when the number of valid particles is less than a threshold, a resampling process is performed, and in this embodiment, the threshold is set to be half of the number of particles. The resampling process is specifically as follows:
sorting each particle in the particle set according to the updated weight value, introducing a distribution function in probability statistics to describe the particle weight, solving a distribution function value corresponding to each particle, namely accumulating the weight values after the weight value sorting, selecting the corresponding particle with the distribution function value before 0.5 to represent a reliable particle, selecting the particle with the distribution function value between 0.5 and 1 to represent a particle to be replaced, extracting the particles with the weight value before three, randomly selecting one to record the weight value as w, and then resampling from the space between [ w,0.5 ]. The resampling process can ensure high confidence of the particles and maintain the diversity of the particles.
And then, calculating the weight of the secondary resampling particles by adopting the principle of the step 2, namely calculating the weight of the resampling particles by combining the actual observed value at the moment k and the pose of the resampling particles, and updating and normalizing to obtain the final weight of the resampling particles.
Step 4, updating the actual pose of the robot at the moment k according to the re-sampling particle weight or the original sampling particle weight updated in the step 3 and the expected value of the current state calculated by using a weighting formula, wherein the method specifically comprises the following steps:
in the pose updating process of this embodiment, the expected value of the current state is calculated by using a weighting formula to update the pose, and the expected value E (x) of the current state can be obtained by using the weighting formula according to the monte carlo sampling conceptk) Namely, the formula is adopted:
wherein,representing the weight of the ith particle at the k moment after normalization;is a dirac function, when the reliable value of the particle is 1, otherwise it is 0; x is the number ofkThe actual pose at time k is shown,representing the pose of the ith particle at the moment k; n represents the number of particles, and when N is large enough, the expectation of the particle description is the current state.
Step 5, updating the mean value and the variance of each particle map by using a Kalman filtering algorithm according to the pose of each particle and the actual observation value at the moment k after updating in the step 1 to obtain map information of all particles; and combining the actual pose of the robot at the moment K obtained in the step 4 with the actual observation value at the moment K and the map information of the particles corresponding to the actual pose of the robot to establish an actual map of the robot. The method comprises the following specific steps:
first, it is necessary to control the particle size of each particlePose positionExpressed as in Kalman updateAnd the actual observed value ztTo update the corresponding particle map, involving updating the mean and variance of the particle map using kalman filtering:
wherein,representing the updated pose of the ith particle at the time t; m is the value of the Kalman gain,showing the estimated pose, z, of the ith particle at time ttRepresenting the actual observed value at time t;the estimated observed value of the ith particle at the time t is shown, and the subscript t-1 shows that the value of the current time t is obtained according to the value of the time t-1;representing the covariance between the estimated pose of the ith particle at the time t and the actual observed value; i represents an identity matrix; h represents a state variable to measurement transition matrix;expressing the estimated pose and the actual observation of the ith particle at the current t moment, which are obtained according to the value of the t-1 momentThe estimated covariance between the values. The time t ∈ k may be equal to or less than the time k.
According toTo obtain map information of the ith particle, where mtMap showing time t, z1:tRepresents the observed value, u, from time 1 to t1:tThe control information indicating the time points 1 to t can be understood as the velocity information of the motion model.
The particle map updating process is shown in fig. 2, and is specifically implemented as follows: by updating the mean and variance of each particle, more accurate positioning can be realized under the condition of simpler environmental features, but when the size of an environmental map is increased, the time and space complexity of the method is greatly increased, and the real-time performance is not ideal. In the transformation, the particles are clustered at specific locations, and the statistical properties of the particles in the vicinity of the location are consistent. Therefore, firstly, the particles are sorted according to the weight of the resampled particles or the weight of the original sampled particles, whether a particle set formed by all the updated particles is empty is judged, if the number of the particles in the particle set is 0, the updating process is ended, otherwise, the particles in the particle set are sequentially taken out, whether the particles are similar to the previous particle is judged, whether the poses and the weights of the two particles are in a set range is judged, namely, the poses and the weights of the two particles are consistent in a certain range, if the similarity condition is met, the updated weight of the previous particle is copied, otherwise, the map updating operation of the particles needs to be carried out by utilizing the Kalman filtering again. By updating the mean and variance of each particle map, map information of all particles can be obtained. And finally, combining the actual pose of the robot at the current moment obtained in the step 4 with the actual observation value at the moment k and the map information of the particles corresponding to the actual pose of the robot to establish an actual map of the robot.
The invention also provides an indoor robot positioning and mapping device, which can adopt the method to position and map. As shown in fig. 3, the apparatus mainly includes: the device comprises a motion control module, a laser radar ranging module and a positioning and mapping module.
The motion control module is used for acquiring the speed information of the robot through the photoelectric encoder and acquiring the pose of each particle at the moment k by combining the speed information of the robot, namely acquiring the pose at the moment k according to the pose at the moment k-1; the pose includes information such as coordinates (x, y) and angle values of the particle.
The laser radar ranging module is used for acquiring the distance and the deviation angle between the robot and a target or an obstacle at the moment k and taking the distance and the deviation angle as the actual observed value at the moment k;
the positioning and mapping module is used for evenly distributing the weight of each particle according to the number of the particles; updating and normalizing the distributed weight of each particle according to the actual observed value at the moment k and the pose of each particle, and calculating to obtain the number of effective particles; when the number of the effective particles is judged to be smaller than the set threshold value, controlling a motion control module to resample the particles, and calculating and updating the weight of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept; according to the re-sampling particle weight or the original sampling particle weight obtained by updating, calculating an expected value of the current state by using a weighting formula to update the actual pose of the robot at the moment k; and establishing an actual map of the robot by combining the obtained actual pose of the robot at the current moment with the actual observation value at the k moment and the map information of the particles corresponding to the actual pose of the robot.
Laser radar ranging module includes 2D laser radar among the device. The motion control module comprises photoelectric encoders, the photoelectric encoders are arranged on two driving wheels of the mobile robot, and the change of the relative position and angle of the robot can be calculated by using the acquired information; and the positioning and mapping module realizes the function of constructing the corresponding particle map according to the information acquired by the laser radar ranging module and the motion control module.
In conclusion, the map is updated according to the updated pose information of the particle sets, the statistical characteristics of the particles are consistent in a group of particle sets in a specific state, one representative particle can be selected according to the population evolution idea, and then other particles only need to be copied, so that the improved method greatly improves the real-time performance of map construction while ensuring the map accuracy.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (8)
1. An indoor robot positioning and mapping method is characterized by comprising the following steps:
step 1, sampling particles based on environmental information, acquiring the pose of each particle at the moment k, and distributing the weight of each particle according to the number of the particles;
step 2, taking the acquired distance and deviation angle between the robot and the target or the obstacle at the moment k as an actual observed value at the moment k, and updating and normalizing the weight of each particle according to the actual observed value at the moment k and the pose of each particle;
step 3, calculating to obtain the number of effective particles according to the weight of each particle updated and normalized in the step 2; when the number of the effective particles is judged to be smaller than a set threshold value, resampling the particles, and calculating and updating the weight of the resampled particles by combining the actual observed value at the moment k and the pose of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept;
step 4, according to the re-sampling particle weight or the original sampling particle weight obtained in the step 3, calculating an expected value of the current state by using a weighting formula to update the actual pose of the robot at the moment k;
step 5, updating the mean value and the variance of each particle map by using a Kalman filtering algorithm according to the pose of each particle at the moment k and the actual observation value at the moment k acquired in the step 1 to obtain map information of all particles; and establishing an actual map of the robot by combining the actual pose of the robot at the moment k updated in the step 4 with the actual observation value at the moment k and the map information of the particles corresponding to the actual pose of the robot.
2. The indoor robot positioning and mapping method of claim 1, wherein the step 1 further comprises determining whether the time k is an initial time, if so, generating the pose of the particles at the initial time, otherwise, obtaining the estimated pose of each particle at the time k according to the pose of the particles in the previous generation of particle set in combination with the motion model.
3. The indoor robot positioning and mapping method of claim 1, wherein the weight of each particle in step 2 is updated by using a formula:
wherein,representing the updated weight of the ith particle at the k moment;representing the weight of the ith particle at the moment of k-1; p represents a probability density function at time k;an actual observed value representing the ith particle at time k;indicating the pose of the ith particle at time k.
4. The indoor robot positioning and mapping method of claim 1, wherein the step 2 normalizes the updated weight of each particle by using a formula:
wherein,representing the weight of the ith particle at the k moment after normalization;representing the updated weight of the ith particle at the k moment; n represents the number of particles.
5. The indoor robot positioning and mapping method of claim 1, wherein the step 3 of calculating the effective particle number NeffThe formula is adopted:
wherein,representing the weight of the ith particle at the k moment after normalization; n represents the number of particles.
6. The indoor robot positioning and mapping method according to claim 1, wherein in step 5, the mean and variance of each particle map are updated by using a kalman filter algorithm, and a formula is adopted:
wherein,representing the updated pose of the ith particle at the time t; m is the value of the Kalman gain,showing the estimated pose, z, of the ith particle at time ttRepresenting the actual observed value at time t;representing the estimated observed value of the ith particle at the time t;representing the covariance between the estimated pose of the ith particle at the time t and the actual observed value; i represents an identity matrix; h represents a conversion matrix;and representing the estimated covariance between the estimated pose of the ith particle at the time t and the actual observed value.
7. The indoor robot positioning and mapping method according to claim 1, wherein the step 5 updates map information of all particles, specifically:
sorting the particles according to the weight of the resampled particles obtained by updating or the weight of the original sampled particles, judging whether a particle set formed by the updated particles is empty or not, if so, ending the updating, otherwise, sequentially taking out the particles in the particle set;
and judging whether each particle in the extracted particle set is similar to the previous particle, namely judging whether the poses and weights of the two particles are consistent in a set range, if so, copying the weight updated by the previous particle, otherwise, updating the mean value and the variance of the particle map by using Kalman filtering again to obtain map information of all the particles.
8. The utility model provides an indoor robot location and picture device of building which characterized in that includes:
the motion control module is used for acquiring the speed information of the robot and acquiring the pose of each particle at the moment k by combining the speed information of the robot;
the laser radar ranging module is used for acquiring the distance and the deviation angle between the robot and the target or the obstacle at the moment k and taking the distance and the deviation angle as the actual observed value at the moment k;
the positioning and mapping module is used for averagely distributing the weight of each particle according to the number of the particles; updating and normalizing the distributed weight of each particle according to the actual observed value at the moment k and the pose of each particle, and calculating to obtain the number of effective particles; when the number of the effective particles is judged to be smaller than the set threshold value, controlling a motion control module to resample the particles, and calculating and updating the weight of the resampled particles; when the number of the effective particles is judged to be larger than the set threshold value, the original sampling particles and the weight of each particle are kept; according to the obtained weight of the resampling particles or the original weight of the sampling particles, calculating an expected value of the current state by using a weighting formula to update the actual pose of the robot at the moment k; updating the mean value and the variance of each particle map by using a Kalman filtering algorithm according to the acquired pose of each particle at the moment k and the actual observed value at the moment k to obtain map information of all particles; and establishing an actual map of the robot by combining the updated actual pose of the robot at the moment k with the actual observed value at the moment k and the map information of the particles corresponding to the actual pose of the robot.
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| CN110702093A (en) * | 2019-09-27 | 2020-01-17 | 五邑大学 | Particle filter-based positioning method and device, storage medium and robot |
| CN110763239A (en) * | 2019-11-14 | 2020-02-07 | 华南智能机器人创新研究院 | Filtering combined laser SLAM mapping method and device |
| CN110909105A (en) * | 2019-11-25 | 2020-03-24 | 上海有个机器人有限公司 | Robot map construction method and system |
| CN110941004A (en) * | 2019-12-04 | 2020-03-31 | 南京航空航天大学 | GNSS/UWB-based indoor and outdoor combined positioning method and device for mobile robot |
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| CN113190564A (en) * | 2020-01-14 | 2021-07-30 | 阿里巴巴集团控股有限公司 | Map updating system, method and device |
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| CN116185046A (en) * | 2023-04-27 | 2023-05-30 | 北京宸普豪新科技有限公司 | Mobile robot positioning method, mobile robot and medium |
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