A method for obtaining environmental data through a robot lidar observation model

Abstract

The present invention provides a method for acquiring environmental data through a robot laser radar observation model, and the specific steps include: S1 establishing a robot laser radar observation model, the observation model includes a normal distribution model, an exponential distribution model and a maximum measurement distribution model; S2 using the observation model to process the environmental data acquired by the laser radar to form results p1, p2 and p3; S23 adding p1, p2, p3 to form environmental data p. The present invention can improve the accuracy of positioning.

Description

Technical Field

[0001] The present invention relates to the field of robot positioning technology, and in particular to a method for acquiring environmental data through a robot laser radar observation model. Background Art

[0002] With the rapid development of robotics technology, robot positioning and mapping solutions based on a variety of sensor technologies have gradually become stable and mature, gradually transforming from traditional tracked magnetic navigation to dynamic path navigation based on the environment. As a result, more and more sensor technology companies have begun to join the track of robot sensor innovation and research and development, such as navigation through lidar. LiDAR can detect and measure the surrounding environment by emitting laser beams and receiving reflected light signals, thereby helping robots to achieve precise navigation and obstacle avoidance.

[0003] In terms of navigation, LiDAR scans the surrounding environment, obtains accurate distance and direction information, and draws a real-time map of the environment around the robot. These maps can help the robot plan its path and ensure that it reaches its destination safely and accurately in a complex factory environment.

[0004] LiDAR plays a vital role in obstacle avoidance. It monitors obstacles around the robot in real time and calculates the distance of the obstacle by measuring the time difference between the emission and reception of the laser beam. At the same time, it calculates the position of the obstacle based on the incident angle and reflection angle of the received laser beam. In this way, the robot can automatically avoid obstacles based on this information, avoid collisions, and ensure safe driving.

[0005] Based on the above principle, LiDAR needs to receive return signals to better realize navigation. However, in reality, temporary moving objects often appear, which is inconsistent with the preset environmental map. In addition, for objects with strong light absorption or objects beyond the range of LiDAR, LiDAR cannot receive the return signal, which will affect the accuracy of navigation. Summary of the invention

[0006] The present invention provides a method for acquiring environmental data through a robot laser radar observation model. The present invention can improve the accuracy of positioning.

[0007] To achieve the above object, the technical solution of the present invention is: a method for obtaining environmental data through a robot laser radar observation model, the specific steps of which include: S1 establishes a robot lidar observation model, which includes a normal distribution model, an exponential distribution model and a maximum measurement distribution model.

[0008] S2 uses the observation model to process the environmental data obtained by the lidar. The steps are as follows: The S21 observation model classifies and stores the environmental data acquired by the laser radar; the classification method is: the normally measured laser beam is input into the normal distribution model, the truncated laser beam generated by the dynamic object is input into the exponential distribution model, and the laser beam whose return beam is not detected by the laser radar is input into the maximum measurement distribution model.

[0009] S22 The normal distribution model parameterizes the environmental data stored therein to form a result p1, the exponential distribution model parameterizes the environmental data stored therein to form a result p2, and the maximum measurement distribution model parameterizes the environmental data stored therein to form a result p3.

[0010] S23 adds p1, p2, and p3 to form environmental data p.

[0011] The beneficial effects of the above method are as follows: by establishing a normal distribution model, an exponential distribution model and a maximum measurement distribution model to respectively process the normal environmental data acquired by the lidar, the environmental data of the truncated laser beam caused by dynamic objects and the environmental data of the laser beam whose return beam is not detected by the lidar, the above environmental data are classified and processed, and the above environmental data are parameterized and added for comparison with the pre-set standard data, thereby improving the accuracy of positioning.

[0012] Furthermore, based on the initial map and the current position of the robot, the current environment is measured by laser radar; a normal distribution model of the laser beam based on the distance of the obstacle measured by the laser radar is established; an exponential distribution model based on whether the laser beam of the laser radar is intercepted by a dynamic object between the laser radar and the obstacle is established; and a maximum measurement distribution model of the laser beam that the laser radar does not detect the return beam is established. This method can establish an observation model in a targeted manner, which is convenient for processing the corresponding data.

[0013] Furthermore, the laser beam whose return beam is not detected by the laser radar includes the laser beam emitted by the laser radar to an object outside the laser beam measurement range and the laser beam emitted by the laser radar to an object with high light absorption.

[0014] Furthermore, the data is processed by the normal distribution model to conform to the normal distribution algorithm. The calculation formula of the normal distribution is:

[0015] in, is the noise in the map, x is the measurement value of each laser beam, and μ is the position point on the map corresponding to the laser beam measurement value x.

[0016] In actual application scenarios, the above settings have the existence of noise. Therefore, by adding reasonable noise, it is possible to ensure that the measured map data matches the actual map data better, thereby facilitating the stable positioning of the robot.

[0017] Furthermore, the data processed by the exponential distribution model conforms to the exponential distribution algorithm. The calculation formula of the exponential distribution is: Where λ is the number of laser beam measurements and x is the measured value of each laser beam.

[0018] The above settings can facilitate the calculation of the number of times the laser radar is truncated during measurement.

[0019] Furthermore, the data is processed by the maximum measurement distribution model to meet the maximum measurement distribution algorithm. The calculation formula of the maximum measurement distribution is: Where x max It is the critical value outside the laser beam measurement range.

[0020] The above settings can facilitate the calculation of the maximum measurement range of the lidar. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is the work flow chart of the present invention.

[0022] Figure 2 It is a calculation function curve diagram of the laser radar observation model of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0024] like Figure 1 to Figure 2 As shown, a method for obtaining environmental data through a robot laser radar observation model includes the following specific steps: S1 presets the map where the robot is located as the initial map and records the environmental data Q of the initial map.

[0025] S2: Establishing a robot laser radar observation model, which is used to receive environmental data acquired by the laser radar, and classify and process the environmental data acquired by the laser radar. The observation model includes a normal distribution model, an exponential distribution model, and a maximum measurement distribution model. In this step, the laser radar is used to measure the current environment based on the initial map and the current position of the robot, and a normal distribution model of the laser beam according to the obstacle distance measured by the laser radar is established, an exponential distribution model is established according to whether the laser beam of the laser radar is intercepted by a dynamic object between the laser radar and the obstacle, and a maximum measurement distribution model of the laser beam that the laser radar does not detect the return beam is established. The laser beam that the laser radar does not detect the return beam includes the laser beam emitted by the laser radar to an object outside the laser beam measurement range and the laser beam emitted by the laser radar to an object with high light absorption, that is, in a large area, if the object exceeds the range of the laser radar, the laser radar cannot receive the reflected signal. In addition, for objects with high light absorption, when the laser beam emitted by the laser radar reaches the object, it is basically absorbed, and the laser radar cannot receive the reflected signal.

[0026] S3 uses the observation model to process the environmental data obtained by the lidar. The steps are as follows: The S31 observation model classifies and stores the environmental data acquired by the laser radar; the classification method is: the normally measured laser beam is input into the normal distribution model, the truncated laser beam generated by the dynamic object is input into the exponential distribution model, and the laser beam whose return beam is not detected by the laser radar is input into the maximum measurement distribution model.

[0027] S32 The normal distribution model parameterizes the environmental data stored therein to form a result p1, the exponential distribution model parameterizes the environmental data stored therein to form a result p2, and the maximum measurement distribution model parameterizes the environmental data stored therein to form a result p3.

[0028] Among them, the algorithm for processing data through the normal distribution model conforms to the normal distribution. The calculation formula of the normal distribution is:

[0029] in, is the noise in the map, x is the measurement value of each laser beam, and μ is the position point on the initial map corresponding to the laser beam measurement value x.

[0030] Processing data through the exponential distribution model conforms to the exponential distribution algorithm. The calculation formula of the exponential distribution is: Where λ is the number of laser beam measurements and x is the measured value of each laser beam.

[0031] The data processed by the maximum measurement distribution model conforms to the algorithm of the maximum measurement distribution. The calculation formula of the maximum measurement distribution is:

[0032] Where x max It is the critical value outside the laser beam measurement range. For example, when the laser beam measurement range is 50m, the critical value outside the laser beam measurement range is 50m.

[0033] S33 Add p1, p2, and p3 to form data p.

[0034] S4 obtains environmental data of the initial map.

[0035] S5 compares p and Q; if the deviation value of p and Q is within the preset range, it means that the current positioning of the robot is accurate; if the deviation value of p and Q is not within the preset range, it means that the current positioning of the robot is unstable and global positioning planning needs to be re-performed.

[0036] In this embodiment, in actual application scenarios, due to the presence of noise, by adding reasonable noise, it is possible to ensure that the measured map data matches the actual map data better, thereby facilitating stable positioning of the robot.

[0037] like Figure 2 As shown in the figure, by establishing the robot's lidar observation model, since the lidar measurement itself has noise, the measurement of the lidar laser beam is approximated to a normal distribution, corresponding to Figure 2 The curve at number 1 is a normal distribution curve. The robot's environment is usually dynamic, while the robot's initial map is static. Objects that are not originally included in the initial map intercept the laser beam, causing the laser radar to accidentally measure a shorter distance. This unexpected phenomenon is treated as an exponential distribution, which corresponds to Figure 2 The curve at number 2 is an exponential distribution curve. Sometimes the laser radar emits a beam that encounters an object with high light absorption or exceeds the range of the laser radar. Since the radar cannot receive the return beam, the measurement will fail completely. This happens frequently, so it is set to obey a uniform measurement distribution, corresponding to Figure 2 The curve at number 3 is a uniform value distribution curve. By combining such distributions, we can get Figure 2 The overall curve is used to establish the lidar observation model of the surrounding obstacles to process the robot's positioning information.

[0038] Step (31) specifically includes subjecting each laser beam of the laser radar measurement laser frame to normal distribution, exponential distribution and uniform distribution respectively and then inputting them into different observation models.

[0039] In this embodiment, by processing data using a normal distribution model, an exponential distribution model, and a maximum measurement distribution model, it is possible to facilitate stable measurement of the robot's surrounding environment, thereby improving positioning accuracy.

Claims

1. A method for acquiring environmental data through a robot laser radar observation model, characterized in that: The specific steps include: S1 establishes a robot laser radar observation model, which includes a normal distribution model, an exponential distribution model, and a maximum measurement distribution model; S2 uses the observation model to process the environmental data obtained by the lidar. The steps are as follows: The S21 observation model classifies and stores the environmental data acquired by the laser radar; the classification method is: the laser beam for normal measurement is input into the normal distribution model, the truncated laser beam generated by the dynamic object is input into the exponential distribution model, and the laser beam for which the laser radar does not detect the return beam is input into the maximum measurement distribution model; S22 The normal distribution model parameters the stored environmental data to form a result p1, the exponential distribution model parameters the stored environmental data to form a result p2, and the maximum measurement distribution model parameters the stored environmental data to form a result p3; S23 adds p1, p2, and p3 to form environmental data p.

2. The method for acquiring environmental data through a robot laser radar observation model according to claim 1, characterized in that: Based on the initial map and the current position of the robot, the current environment is measured by laser radar; a normal distribution model of the laser beam based on the obstacle distance measured by the laser radar is established; an exponential distribution model is established based on whether the laser beam of the laser radar is intercepted by a dynamic object between the laser radar and the obstacle; and a maximum measurement distribution model of the laser beam based on the return beam not detected by the laser radar is established.

3. The method for acquiring environmental data through a robot laser radar observation model according to claim 2, characterized in that: The laser beams for which the laser radar does not detect a return beam include the laser beams emitted by the laser radar to an object outside the measurement range of the laser beam and the laser beams emitted by the laser radar to an object with high light absorption.

4. The method for acquiring environmental data through a robot laser radar observation model according to claim 1, characterized in that: The algorithm for processing data through the normal distribution model conforms to the normal distribution. The calculation formula of the normal distribution is: in, is the noise in the map, x is the measurement value of each laser beam, and μ is the position point on the initial map corresponding to the laser beam measurement value x.

5. The method for acquiring environmental data through a robot laser radar observation model according to claim 1, characterized in that: Processing data through the exponential distribution model conforms to the exponential distribution algorithm. The calculation formula of the exponential distribution is: Where λ is the number of laser beam measurements and x is the measured value of each laser beam.

6. The method for acquiring environmental data through a robot laser radar observation model according to claim 1, characterized in that: The data processed by the maximum measurement distribution model conforms to the algorithm of the maximum measurement distribution. The calculation formula of the maximum measurement distribution is: Where x max It is the critical value outside the laser beam measurement range.

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