A cliff detection method, intelligent vehicle, robot and storage medium

Abstract

The present invention discloses a cliff detection method, an intelligent vehicle, a robot and a storage medium, and the method includes: scanning a region to be measured by a three-dimensional laser sensor to obtain laser point cloud data; searching for target single-frame laser data from the laser point cloud data; grouping the laser scanning points in the target single-frame laser data to obtain the first and second laser arrays according to the spatial coordinates of the laser center point of the laser point cloud data and the target laser scanning point in a preset two-dimensional coordinate system; clustering the laser scanning points in the first laser array and the second laser array to obtain the first and second cluster arrays; judging whether there is a cliff in the region to be measured according to the target laser scanning point, the laser center point and the first and second cluster arrays. The present invention improves the accuracy of cliff detection and enhances driving safety.

Description

Technical Field

[0001] The present invention relates to the field of robot navigation technology, and further to a cliff detection method, an intelligent vehicle, a robot and a storage medium. Background Art

[0002] With the development of robot navigation technology, mobility is no longer just a requirement for robots used in production. Nowadays, robots, including smart sweeping robots, have increasingly higher requirements for navigation and obstacle avoidance stability. However, the robot working environment is relatively complex, especially in indoor environments with steps, stairs, steep slopes, pits and other environmental factors. If there is no effective anti-fall method, the robot can easily be severely damaged in the fall.

[0003] In the existing technology, most of the anti-falling methods are to use infrared sensors to send coded signals in different directions. If no reflected signal is received within a predetermined time, the robot stops moving forward to prevent falling. The second method is to combine infrared sensors and ultrasonic sensors to obtain the distance between the robot and the obstacle, so as to determine whether the robot is within the determined falling distance range. If so, the robot stops moving forward to prevent falling. Summary of the invention

[0004] In view of the above technical problems, the purpose of the present invention is to solve the technical problem of being unable to accurately detect suspended or floating obstacles and reduce the risk of the robot falling.

[0005] In order to achieve the above object, the present invention provides a cliff detection method, comprising the steps of:

[0006] The laser point cloud data is obtained by scanning the area to be measured by a three-dimensional laser sensor; the laser point cloud data includes several groups of single-frame laser data;

[0007] Searching for target single-frame laser data from the laser point cloud data; the target single-frame laser data includes a target laser scanning point with a minimum height value in a preset two-dimensional coordinate system;

[0008] According to the laser center point of the laser point cloud data and the spatial coordinates of the target laser scanning point in the preset two-dimensional coordinate system, the laser scanning points in the target single-frame laser data are grouped to obtain the first and second laser arrays; wherein all the laser scanning points in the first and second laser arrays are enclosed to form the target single-frame laser data;

[0009] Perform clustering processing on the laser scanning points in the first laser array and the second laser array to obtain the first and second clustering arrays respectively;

[0010] It is determined whether there is a cliff in the area to be measured according to the target laser scanning point, the laser center point and the first and second cluster arrays.

[0011] In some implementations, searching for target single-frame laser data from the laser point cloud data comprises the steps of:

[0012] Obtaining coordinate data of all laser scanning points in the single frame of laser data in a laser coordinate system;

[0013] The spatial coordinates of each laser scanning point in a preset two-dimensional coordinate system are calculated based on the coordinate data and the conversion matrix; the conversion matrix is ​​the conversion relationship between the laser coordinate system and the preset two-dimensional coordinate system;

[0014] Compare the height values ​​of all laser scanning points in the preset two-dimensional coordinate system according to the spatial coordinates, and find the target laser scanning point with the smallest height value;

[0015] The single frame of laser data to which the target laser scanning point belongs is determined as the target single frame of laser data.

[0016] In some embodiments, clustering the laser scanning points in the first laser array and the second laser array to obtain the first and second clustered arrays includes the following steps:

[0017] Obtaining the position coordinates of each laser scanning point in the first and second laser arrays in the carrier coordinate system;

[0018] Determine, according to the position coordinates, whether a first distance value between each pair of adjacent laser scanning points in the first and second laser arrays is greater than a preset first distance threshold, and determine whether a height value between each pair of adjacent laser scanning points is greater than a preset height threshold;

[0019] According to the judgment result, the cluster point pairs corresponding to the first and second laser arrays are found; the cluster point pairs are two adjacent laser scanning points whose first distance value is greater than a preset first distance threshold and whose height value is greater than a preset height threshold;

[0020] According to the clustering point pairs and the target laser scanning points, the first and second laser arrays are grouped to obtain first and second clustering arrays.

[0021] In some embodiments, judging whether there is a cliff in the test area according to the target laser scanning point, the laser center point and the first and second cluster arrays includes the steps of:

[0022] Determine two target cluster points close to the target laser scanning point according to the cluster point pairs corresponding to the first and second laser arrays respectively;

[0023] According to the position coordinates of the target laser scanning point and the target clustering point, connecting lines to generate first and second slope line segments respectively;

[0024] According to the position coordinates of the target laser scanning point and the two target clustering points, projecting them onto the central plane where the laser center point is located to obtain the first and second projection points;

[0025] Generate a projected clustered line segment according to the first and second projection points;

[0026] According to the projected clustered line segment, a candidate laser set located within a preset distance value range below the projected clustered line segment is screened out from the second clustered array;

[0027] Calculating a second distance value between each laser scanning point in the candidate laser set and the projected clustered line segment, and calculating a third distance value between each laser scanning point in the candidate laser set and any target clustered point;

[0028] Determine whether there is a candidate laser scanning point whose corresponding second distance value is greater than the second distance threshold, and determine whether there is a candidate laser scanning point whose corresponding third distance value is greater than its own maximum contour diameter;

[0029] If there is a candidate laser scanning point whose corresponding second distance value is greater than the second distance threshold, and there is a candidate laser scanning point whose corresponding third distance value is greater than the maximum contour diameter, it is determined that there is a cliff in the area to be measured.

[0030] In some embodiments, the method of determining whether there is a cliff in the test area according to the laser data corresponding to the two laser arrays includes the following steps:

[0031] If there is a cliff in the area to be measured, a virtual data wall is marked and generated at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning points.

[0032] In some embodiments, the step of marking and generating a virtual data wall at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning points comprises the following steps:

[0033] Calculate the predicted value according to the height value in the position coordinates of the candidate laser scanning point;

[0034] According to the position coordinates of the candidate laser scanning points, a virtual data wall with a height equal to the predicted value is marked and generated in the environment map.

[0035] According to another aspect of the present invention, the present invention further provides an intelligent vehicle, including a three-dimensional laser sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program stored in the memory to implement the operations performed by the cliff detection method.

[0036] According to another aspect of the present invention, the present invention further provides a robot, including a three-dimensional laser sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is used to execute the computer program stored in the memory to implement the operations performed by the cliff detection method.

[0037] According to another aspect of the present invention, the present invention further provides a storage medium, wherein the storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operation performed by the cliff detection method.

[0038] Compared with the prior art, the cliff detection method, intelligent vehicle, robot and storage medium provided by the present invention can identify whether there are cliffs (steps, stairs, steep slopes, pits, etc.) in the area to be tested through laser point cloud data collected by a three-dimensional laser sensor, thereby improving the accuracy of the robot or intelligent vehicle in judging the cliff, reducing the misjudgment rate of cliff detection, and improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0040] Figure 1 is a flow chart of an embodiment of a cliff detection method of the present invention;

[0041] Figure 2 is a schematic diagram of laser point cloud data of the present invention;

[0042] Figure 3 is a schematic diagram of a single frame of laser data of a target of the present invention;

[0043] Figure 4 is a schematic diagram of the first and second clustering arrays of the present invention;

[0044] Figure 5 It is a schematic diagram of a marked virtual data wall of a cliff detection method of the present invention. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0046] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections.

[0047] In order to simplify the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0048] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0049] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0051] Reference Manual Attached Figure 1-Figure 4 , a cliff detection method, specifically, comprises the steps of:

[0052] S100 scans the area to be measured by a three-dimensional laser sensor 2 to obtain laser point cloud data; the laser point cloud data includes several groups of single-frame laser data;

[0053] Specifically, the laser sensor is a device that collects and acquires laser point cloud data around the robot 1 by emitting laser beams. The laser point cloud data collected by the laser sensor includes several groups of single-frame laser data corresponding to the area to be measured, wherein each group of single-frame laser data includes laser scanning points and their characteristic attribute data, and the characteristic attribute data includes the coordinate data of the laser scanning points in the laser coordinate system O2-XYZ, the attribute characteristics of the laser scanning points, etc. In this way, the environmental characteristic information around the robot 1 or the intelligent vehicle can be analyzed based on the coordinate data and attribute characteristics of the laser scanning points. The environmental characteristic information includes but is not limited to obstacle type, obstacle distance, angle, and contour characteristics. The laser sensor can be implemented by any device that can emit a laser beam, such as a laser radar.

[0054] The robot 1 or the smart vehicle is equipped with a three-dimensional laser sensor 2. The specific installation position of the three-dimensional laser sensor 2 on the robot 1 or the smart vehicle can be set according to business needs and is not limited here. During the driving process, the robot 1 or the smart vehicle of the present invention emits a laser beam to the area to be measured in front of the driving direction through the three-dimensional laser sensor 2, so as to scan and obtain laser point cloud data.

[0055] For example, the Mid 70 laser radar is a type of three-dimensional laser sensor 2. Unlike conventional 3D laser radars, the Mid 70 laser radar is a non-repetitive scanning laser radar. By accumulating laser data, the robot 1 or the intelligent vehicle can achieve full coverage detection in front. For example, the laser point cloud data collected by the Mid 70 laser radar is as follows: Figure 2 As shown in the figure, the collected laser point cloud data is composed of Figure 3 The single frame of laser data shown consists of, for example, Figure 2 The single-frame laser data M1 and the single-frame laser data M2 shown are as follows: Figure 2 The other single-frame laser data in the laser point cloud data are not marked, and the laser point cloud data includes a large number of single-frame laser data. That is to say, the laser point cloud data of the present invention includes multiple sets of single-frame laser data around the laser center point O, wherein the multiple sets of single-frame laser data around the laser center point O eventually fully cover the front to achieve detection of the front environment.

[0056] S200 searches for target single-frame laser data from the laser point cloud data; the target single-frame laser data includes a target laser scanning point P with the smallest height value in a preset two-dimensional coordinate system O-UV;

[0057] Specifically, the conversion relationship between the preset two-dimensional coordinate system O-UV and the laser coordinate system O2-XYZ can be calculated and obtained. This calculation method is similar to the conversion relationship between the camera coordinate system and the pixel coordinate system. The laser coordinate system O2-XYZ can be reduced in dimension, that is, the laser coordinate system O2-XYZ is projected onto the XZ axis plane of the laser coordinate system O2-XYZ. In this way, after the laser point cloud data including several groups of single-frame laser data are acquired through the three-dimensional laser sensor 2, the spatial coordinates of each laser scanning point in the preset two-dimensional coordinate system O-UV can be calculated and obtained according to the coordinate data of each laser scanning point in the laser coordinate system O2-XYZ, and then the spatial coordinates of each laser scanning point in the preset two-dimensional coordinate system O-UV can be compared, and the target laser scanning point P with the smallest height value can be found, and it is determined that the single-frame laser data including the target laser scanning point P is the target single-frame laser data.

[0058] S300, according to the spatial coordinates of the laser center point O of the laser point cloud data and the target laser scanning point P on the preset two-dimensional coordinate system O-UV, the laser scanning points in the target single-frame laser data are grouped to obtain the first and second laser arrays; wherein all the laser scanning points in the first and second laser arrays are enclosed to form the target single-frame laser data;

[0059] Specifically, the laser center point O and the target laser scanning point P of the laser point cloud data are taken as critical nodes, and the laser scanning points in the target single-frame laser data are grouped to obtain a first laser array including laser scanning points within a range from the laser center point O to the target laser scanning point P, and a second laser array including laser scanning points within a range from the target laser scanning point P to the laser center point O.

[0060] Among them, the shape obtained by enclosing and connecting all adjacent laser scanning points in each group of single-frame laser data is a closed ring or petal shape. The distribution characteristics of the spatial coordinates of all laser scanning points in each group of single-frame laser data on the preset two-dimensional coordinate system O-UV are as follows: Figure 3 As shown, it goes from the laser center point O(0, 0) to the vertex P(ud, vd) and then returns to the laser center point O(0, 0). Therefore, the dividing line segment L3 can be generated by connecting the laser scanning point and the laser center point O, and the laser scanning points in the target single frame laser data are grouped according to the dividing line segment to obtain the first and second laser arrays.

[0061] For example, Figure 3 As shown, the laser scanning point and the laser center point O are connected to generate a dividing line segment L3, and the single frame laser data Mt is divided into Figure 3 The first laser array S1 is shown, and Figure 3The second laser array S2 is shown.

[0062] S400: performing clustering processing on the laser scanning points in the first laser array and the second laser array to obtain a first clustering array and a second clustering array respectively;

[0063] S500 determines whether there is a cliff in the area to be detected according to the target laser scanning point P, the laser center point O, and the first and second cluster arrays.

[0064] Specifically, the laser scanning points in the first laser array are clustered to obtain a first clustered array, and the laser scanning points in the second laser array are clustered to obtain a second clustered array. Then, the robot 1 or the intelligent vehicle analyzes and determines whether there is a cliff in the test area based on the target laser scanning point P, the laser center point O, and the first and second clustered arrays.

[0065] The present invention installs a three-dimensional laser sensor 2 in front of the robot 1 or the intelligent vehicle. Since the robot 1 or the intelligent vehicle may move to the stairwell or escalator during operation, there is a risk of falling if the height difference of the step in front is not detected in time. The present invention uses the laser point cloud data collected by the three-dimensional laser sensor 2 to identify whether there is a cliff (steps, stairs, steep slopes, pits, etc.) in the area to be tested. The present invention dynamically sets the limiting conditions for cliff detection according to the state change information of the ground, and eliminates the interference factors that affect cliff detection due to environmental reflection or noise caused by infrared sensors or ultrasonic sensors in the prior art, so as to improve the accuracy of the robot 1 or the intelligent vehicle in judging the cliff and reduce the misjudgment rate of cliff detection.

[0066] In one embodiment, a cliff detection method includes the following steps:

[0067] S100 scans the area to be measured by a three-dimensional laser sensor 2 to obtain laser point cloud data; the laser point cloud data includes several groups of single-frame laser data;

[0068] S210 obtains coordinate data of all laser scanning points in the single frame of laser data in the laser coordinate system O2-XYZ;

[0069] S220, calculating the spatial coordinates of each laser scanning point in a preset two-dimensional coordinate system O-UV according to the coordinate data and the conversion matrix; the conversion matrix is ​​a conversion relationship between the laser coordinate system O2-XYZ and the preset two-dimensional coordinate system O-UV;

[0070] S230 compares the height values ​​of all laser scanning points in the preset two-dimensional coordinate system O-UV according to the spatial coordinates, and finds the target laser scanning point P with the smallest height value;

[0071] S240 determines that the single frame laser data to which the target laser scanning point P belongs is the target single frame laser data; the target single frame laser data includes the target laser scanning point P with the smallest height value under the preset two-dimensional coordinate system O-UV;

[0072] Specifically, the conversion relationship between the preset two-dimensional coordinate system O-UV and the laser coordinate system O2-XYZ can be calculated and obtained. This calculation method is similar to the conversion relationship between the camera coordinate system and the pixel coordinate system. The laser coordinate system O2-XYZ can be reduced in dimension, that is, the laser coordinate system O2-XYZ is projected onto the XZ axis plane of the preset two-dimensional coordinate system O-UV. In this way, after the laser point cloud data including several groups of single-frame laser data are acquired through the three-dimensional laser sensor 2, the spatial coordinates of each laser scanning point in the preset two-dimensional coordinate system O-UV can be calculated and obtained according to the coordinate data of each laser scanning point in the laser coordinate system O2-XYZ, and then the spatial coordinates of each laser scanning point in the preset two-dimensional coordinate system O-UV are compared, and the target laser scanning point P with the smallest height value is found, and it is determined that the single-frame laser data including the target laser scanning point P is the target single-frame laser data.

[0073] Assume that the X-axis of the laser coordinate system O2-XYZ is the left-right direction of the robot 1 or the intelligent vehicle, the Y-axis is the height direction of the robot 1 or the intelligent vehicle, and the Z-axis is the forward direction of the robot 1 or the intelligent vehicle. The U-axis of the preset two-dimensional coordinate system O-UV is the left-right direction of the robot 1 or the intelligent vehicle, and the Y-axis is the height direction of the robot 1 or the intelligent vehicle. If the coordinate data of any laser scanning point N in the laser point cloud data under the laser coordinate system O2-XYZ is (xn, yn, zn), then the spatial coordinates of the laser scanning point N under the preset two-dimensional coordinate system O-UV can be obtained by the above conversion calculation as (un, vn). In this way, the spatial coordinates of all laser scanning points in the laser point cloud data under the preset two-dimensional coordinate system O-UV can be calculated and obtained, and the coordinate values ​​of all laser scanning points on the V-axis are compared. The coordinate values ​​on the V-axis represent the height values ​​under the preset two-dimensional coordinate system O-UV. Therefore, the laser scanning point corresponding to the coordinate value on the V-axis is found to be the target laser scanning point P. Then, it is determined that the single-frame laser data including the target laser scanning point P is the target single-frame laser data.

[0074] For example, the laser point cloud data collected by the robot 1 or the intelligent vehicle is as follows: Figure 2 As shown, in the preset two-dimensional coordinate system O-UV, the coordinate value of the laser scanning point in the single-frame laser data Mt on the V axis is the smallest, so the laser scanning point (up, vp) is the target laser scanning point P.

[0075] S300, according to the spatial coordinates of the laser center point O of the laser point cloud data and the target laser scanning point P on the preset two-dimensional coordinate system O-UV, the laser scanning points in the target single-frame laser data are grouped to obtain the first and second laser arrays; wherein all the laser scanning points in the first and second laser arrays are enclosed to form the target single-frame laser data;

[0076] S400: performing clustering processing on the laser scanning points in the first laser array and the second laser array to obtain a first clustering array and a second clustering array respectively;

[0077] S500 determines whether there is a cliff in the area to be detected according to the target laser scanning point P, the laser center point O, and the first and second cluster arrays.

[0078] Specifically, this embodiment is an optimized embodiment of the above embodiment, and the parts in this embodiment that are the same as the above embodiment will not be repeated one by one. The present invention installs a three-dimensional laser sensor 2 in front of the robot 1 or the intelligent vehicle. Since the robot 1 or the intelligent vehicle may move to the stairwell or escalator during operation, there is a risk of falling if the height difference of the steps in front is not detected in time. The present invention uses the laser point cloud data collected by the three-dimensional laser sensor 2 to identify whether there is a cliff (steps, stairs, steep slopes, pits, etc.) in the area to be tested. The present invention dynamically sets the limiting conditions for cliff detection according to the state change information of the ground, and eliminates the interference factors that affect cliff detection due to environmental reflection or noise caused by the use of infrared sensors or ultrasonic sensors in the prior art, so as to improve the accuracy of the robot 1 or the intelligent vehicle in judging the cliff and reduce the misjudgment rate of cliff detection.

[0079] In one embodiment, a cliff detection method includes the following steps:

[0080] S100 scans the area to be measured by a three-dimensional laser sensor 2 to obtain laser point cloud data; the laser point cloud data includes several groups of single-frame laser data;

[0081] S210 obtains coordinate data of all laser scanning points in the single frame of laser data in the laser coordinate system O2-XYZ;

[0082] S220, calculating the spatial coordinates of each laser scanning point in a preset two-dimensional coordinate system O-UV according to the coordinate data and the conversion matrix; the conversion matrix is ​​a conversion relationship between the laser coordinate system O2-XYZ and the preset two-dimensional coordinate system O-UV;

[0083] S230 compares the height values ​​of all laser scanning points in the preset two-dimensional coordinate system O-UV according to the spatial coordinates, and finds the target laser scanning point P with the smallest height value;

[0084] S240 determines that the single frame laser data to which the target laser scanning point P belongs is the target single frame laser data; the target single frame laser data includes the target laser scanning point P with the smallest height value under the preset two-dimensional coordinate system O-UV;

[0085] S300, according to the spatial coordinates of the laser center point O of the laser point cloud data and the target laser scanning point P on the preset two-dimensional coordinate system O-UV, the laser scanning points in the target single-frame laser data are grouped to obtain the first and second laser arrays; wherein all the laser scanning points in the first and second laser arrays are enclosed to form the target single-frame laser data;

[0086] S410 obtains the position coordinates of each laser scanning point in the first and second laser arrays in the carrier coordinate system O1-XYZ;

[0087] Specifically, the carrier coordinate system O1-XYZ refers to the robot coordinate system (for example, a three-dimensional coordinate system established with the robot center point as the origin) or the intelligent vehicle coordinate system (for example, a three-dimensional coordinate system established with the intelligent vehicle point as the origin). Since the conversion relationship between the carrier coordinate system O1-XYZ and the laser coordinate system O2-XYZ can be calculated and obtained, the conversion between the carrier coordinate system O1-XYZ and the laser coordinate system O2-XYZ is a prior art and will not be described in detail here. In short, after the three-dimensional laser sensor 2 scans and obtains the laser point cloud data, the position coordinates of each laser scanning point in the first and second laser arrays in the carrier coordinate system O1-XYZ can be converted and calculated according to the coordinate data of each laser scanning point in the laser point cloud data under the laser coordinate system O2-XYZ.

[0088] S420: determining, based on the position coordinates, whether a first distance value between each pair of adjacent laser scanning points in the first and second laser arrays is greater than a preset first distance threshold, and determining whether a height value between each pair of adjacent laser scanning points is greater than a preset height threshold;

[0089] Specifically, the coordinate data of any laser scanning point N in the laser coordinate system O2-XYZ is (xn, yn, zn), then the position coordinates of the laser scanning point N in the carrier coordinate system O1-XYZ obtained by conversion calculation are (Xn, Yn, Zn). According to the two-point distance formula in the three-dimensional coordinate system, the first distance value between the first pair of adjacent laser scanning points can be calculated. Similarly, the first distance value between all adjacent laser scanning points can be calculated. Therefore, after calculating the first distance values ​​between all adjacent laser scanning points, it is determined whether the first distance value between each pair of adjacent laser scanning points is greater than the preset first distance threshold, and the corresponding judgment result is output.

[0090] In addition, according to the position coordinates of the laser scanning point N in the carrier coordinate system O1-XYZ as (Xn, Yn, Zn), the coordinate values ​​of the Y axis of the adjacent laser scanning points in the carrier coordinate system O1-XYZ are subtracted, and the absolute value of the difference is the height value between the adjacent laser scanning points. Similarly, the height values ​​between all adjacent laser scanning points can be calculated. Therefore, after calculating the height values ​​between all adjacent laser scanning points, it is determined whether the height value between each pair of adjacent laser scanning points is greater than the preset height threshold, and the corresponding judgment result is output.

[0091] Exemplarily, assume that the set of the first laser array is represented by S1 = {T1, T2, ..., Ti}, and the set of the second laser array is represented by S2 = {K1, K2, ..., Kj}, Ti is the number of the laser scanning point in the first laser array, Kj is the number of the laser scanning point in the second laser array, and i and j are both natural numbers. In addition, all the laser scanning points in the first laser array S1 = {T1, T2, ..., Ti} are arranged in order of the size of the number, and all the laser scanning points in the second laser array S2 = {K1, K2, ..., Kj} are arranged in order of the size of the number. If the position coordinates of the laser scanning point K1 in the carrier coordinate system O1-XYZ are (XK1, YK1, ZK1), and the position coordinates of the laser scanning point K2 adjacent to the laser scanning point K1 in the carrier coordinate system O1-XYZ are (XK2, YK2, ZK2), then the distance value between adjacent laser scanning points K1 and K2 can be calculated according to the two-point distance formula, and the height value △h between adjacent laser scanning points K1 and K2 is |YK1-YK2|.

[0092] S430 finds out the clustered point pairs corresponding to the first and second laser arrays respectively according to the judgment result; the clustered point pairs are two adjacent laser scanning points whose first distance value is greater than a preset first distance threshold and whose height value is greater than a preset height threshold;

[0093] Specifically, according to the above judgment results, two adjacent laser scanning points in the first laser array whose first distance value is greater than the preset first distance threshold and whose height value is greater than the preset height threshold are found, and it is determined that the two adjacent laser scanning points are the clustered point pair corresponding to the first laser array. Similarly, according to the judgment results, two adjacent laser scanning points in the second laser array whose first distance value is greater than the preset first distance threshold and whose height value is greater than the preset height threshold are found, and it is determined that the two adjacent laser scanning points are the clustered point pair corresponding to the second laser array.

[0094] For example, Figure 4As shown, q1 and q3 are two adjacent laser scanning points in the first laser array whose first distance value is greater than the preset first distance threshold and whose height value is greater than the preset height threshold, and q2 and q4 are two adjacent laser scanning points in the second laser array whose first distance value is greater than the preset first distance threshold and whose height value is greater than the preset height threshold. In other words, the first cluster point pair corresponding to the first laser array is q1 and q3, and the second cluster point pair corresponding to the second laser array is q2 and q4.

[0095] S440, grouping the first and second laser arrays according to the cluster point pairs and the target laser scanning point P to obtain first and second cluster arrays;

[0096] Specifically, Figure 4 As shown, according to the first cluster point pair corresponding to the first laser array including cluster point q1 and cluster point q3, and the second cluster point pair corresponding to the second laser array including cluster point q2 and cluster point q4, the first laser array and the second laser array are grouped to obtain a first cluster array and a second cluster array. Figure 4 The second clustering array is shown as follows: Figure 4 The laser scanning points are shown as including the range from cluster point q3 →laser center point O →cluster point q4.

[0097] S510, determining two target cluster points close to the target laser scanning point P according to the cluster point pairs corresponding to the first and second laser arrays respectively;

[0098] Specifically, according to the position coordinates of the cluster point pairs corresponding to the first and second laser arrays in the carrier coordinate system O1-XYZ, and the position coordinates of the target laser scanning point P in the carrier coordinate system O1-XYZ, the distances between the two cluster points corresponding to the first laser array (i.e., the first cluster point pair) and the target laser scanning point P are calculated and compared, and the cluster point with a smaller distance is determined to be the target cluster point in the first laser array close to the target laser scanning point P. Similarly, the target cluster point in the second laser array close to the target laser scanning point P can be determined.

[0099] For example, Figure 4As shown, the cluster point pair corresponding to the first laser array includes cluster point q1 and cluster point q3, and the cluster point pair corresponding to the second laser array includes cluster point q2 and cluster point q4. Among them, since cluster point q1 in the cluster point pair is close to the target laser scanning point P, and cluster point q3 in the cluster point pair is far away from the target laser scanning point P, the target cluster point of the first laser array is cluster point q1. Since cluster point q2 in the cluster point pair is close to the target laser scanning point P, and cluster point q4 in the cluster point pair is far away from the target laser scanning point P, the target cluster point of the second laser array is cluster point q2.

[0100] S520, according to the position coordinates of the target laser scanning point P and the two target clustering points, respectively connect the first and second slope line segments;

[0101] S530, projecting the target laser scanning point P and the position coordinates of the two target clustering points onto the central plane 3 where the laser center point O is located to obtain the first and second projection points;

[0102] S540 generates a projection clustering line segment L2 according to the first and second projection points;

[0103] Specifically, in the carrier coordinate system O1-XYZ, according to the position coordinates of the target laser scanning point P and the two target clustering points, the target laser scanning point P is connected with the target clustering point of the first laser array (for example, clustering point q1) to generate a first slope segment, and the target laser scanning point P is connected with the target clustering point of the second laser array (for example, clustering point q2) to generate a second slope segment.

[0104] Then, the target clustering point (e.g., clustering point q1) of the first laser array and the target clustering point (e.g., clustering point q2) of the second laser array are connected to generate the target clustering line segment L1, and a reference line segment L4 is drawn that passes through the target laser scanning point P and is perpendicular to the target clustering line segment L1. The first projected line segment L5 is connected to pass through the target clustering point (e.g., clustering point q1) of the first laser array and is parallel to the reference line segment L4, and the second projected line segment L6 is connected to pass through the target clustering point (e.g., clustering point q2) of the second laser array and is parallel to the reference line segment L4. The first projected line segment L5 is extended to intersect with the central plane 3 where the laser center point is located to obtain the first projected clustering point q1′, and the second projected line segment L6 is extended to intersect with the central plane 3 where the laser center point is located to obtain the second projected clustering point q2′. Finally, the first projected clustering point q1′ and the second projected clustering point q2′ are connected to generate the projected clustering line segment L2.

[0105] Specifically, Figure 4 and Figure 5For example, the cluster slopes of the first and second slope segments corresponding to point P to point q1 or point q2 are calculated. Since point P is a target laser scanning point close to robot 1 or intelligent vehicle, it can be considered that the cluster slopes of the first and second slope segments corresponding to point P to point q1 or point q2 are the slopes of the ground, which is equivalent to finding the ground data in the point cloud. Among them, the slope of the cluster where the target laser scanning point P is located is calculated by straight line fitting, that is, assuming that the cluster where the target laser scanning point P is located is the point set Q = {p, p1, p2, ..., q2}, if the coordinates of each laser scanning point in the point set Q are p = (x1, y1, z1), p2 = (x2, y2, z2), ..., q2 = (xn, yn, zn), the least squares method is used to fit the spatial straight line to obtain the straight line equation Ax + By + Cz + D = 0, where the parameters A, B, C, D are the coefficients of the straight line equation corresponding to the first or second slope line segment, and the corresponding road slope is arctan (-B / A). For example, the first slope straight line equation from point P to point q1 is obtained by using the laser scanning points between point P and point q1 in the point set Q and the least squares method to fit the spatial straight line. Similarly, the second slope straight line equation from point P to point q2 can be obtained by using the laser scanning points between point P and point q2 in the point set Q and the least squares method to fit the spatial straight line.

[0106] S550, screening out a candidate laser set located within a preset distance value range below the projected clustering line segment L2 from the second clustering array according to the projected clustering line segment L2;

[0107] Specifically, Figure 4 As shown, the second clustering array includes laser scanning points in the range from clustering point q3→laser center point O→clustering point q4, and then, a candidate laser set located below the projected clustering line segment L2 is screened out from the second clustering array (for example, candidate laser scanning points in the range from the first projected clustering point q1′→the second projected clustering point q2′→clustering point q4→clustering point q3), or a candidate laser set located within a preset distance value range below the projected clustering line segment L2 is screened out from the second clustering array.

[0108] S560: Calculate the second distance value between each laser scanning point in the candidate laser set and the projected clustering line segment L2, and calculate the third distance value between each laser scanning point in the candidate laser set and any target clustering point;

[0109] S560 determines whether there is a candidate laser scanning point corresponding to a second distance value greater than a second distance threshold, and determines whether there is a candidate laser scanning point corresponding to a third distance value greater than its own maximum contour diameter;

[0110] S570: If there is a candidate laser scanning point whose corresponding second distance value is greater than the second distance threshold, and there is a candidate laser scanning point whose corresponding third distance value is greater than the maximum contour diameter, it is determined that there is a cliff in the area to be measured;

[0111] S600: If there is a cliff in the area to be measured, a virtual data wall 4 is marked and generated at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning points.

[0112] In some embodiments, if there is a cliff in the area to be measured, S600, marking and generating a virtual data wall 4 at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning point includes the following steps:

[0113] S610: if there is a cliff in the area to be measured, a predicted value is calculated according to the height value in the position coordinates of the candidate laser scanning point;

[0114] S620: Marking and generating a virtual data wall 4 with a height equal to the predicted value in the environment map according to the position coordinates of the candidate laser scanning point.

[0115] Specifically, refer to the above-mentioned embodiment to calculate the slope straight line equations from point P to point q1 and from point P to point q2, and extend the line segment corresponding to the slope straight line equation to project it to the plane where point O is located, and then find the candidate laser scanning point W whose second distance value is greater than the second distance threshold and whose third distance value is greater than the maximum contour diameter from the candidate laser set. If a candidate laser scanning point W is located below a preset distance value range (for example, 5 cm) below the projected clustering line segment L2, and the distance between the candidate laser scanning point W and point q1 or q2 is greater than the maximum contour diameter (for example, the diameter of the maximum contour of the robot 1 or the intelligent vehicle), the height value of the candidate laser scanning point W in the carrier coordinate system O1-XYZ is subjected to a linear height inversion calculation, and then the candidate laser scanning point W is transferred to points q1 and q2, so as to finally form a virtual data wall 4 with a certain height at the nearest step. Exemplarily, as follows Figure 5 The height of the line segment L4 shown is the height of the virtual data wall 4. The virtual data wall 4 will be marked in the environment map so that it can be easily regarded as an obstacle by the robot 1 or the intelligent vehicle, thereby preventing the robot 1 or the intelligent vehicle from falling.

[0116] The height value of the candidate laser scanning point W in the carrier coordinate system O1-XYZ is calculated by linear height inversion, and then the candidate laser scanning point W is transferred to the point q1, q2. The specific process is as follows: the first slope straight line equation from point P to point q1 (or the second slope straight line equation from point P to point q2) is calculated by the above embodiment, and the Figure 5Substitute the position coordinates of the candidate laser scanning point W in the range of the first projected cluster point q1′→the second projected cluster point q2′→the cluster point q4→the cluster point q3 into the first slope straight line equation (or the second slope straight line equation), and calculate the distance from each candidate laser scanning point W to the straight line according to the distance formula from the point to the spatial straight line, which is the height h of the candidate laser scanning point W in the carrier coordinate system O1-XYZ. After calculating the height h of the candidate laser scanning point W in the carrier coordinate system O1-XYZ, if the height h of the candidate laser scanning point W in the carrier coordinate system O1-XYZ is greater than 5cm and the Z-axis coordinate value z of the candidate laser scanning point W in the carrier coordinate system is less than 0, use the position coordinates of point q2 in the carrier coordinate system to perform an inverse calculation to obtain the position coordinates corresponding to the inversion point w'. For example, if the position coordinates of point q2 are (x, y, z), then the coordinates of the reversal point w' corresponding to point q2 are (x, y, z+h), so that point w' is reversed to just above q2, forming a virtual data wall 4.

[0117] For example, Figure 4 The first slope straight line corresponding to point P to point q1' is projected onto the XZ plane, that is, the first slope straight line equation Ax+By+Cz+D=0 in which y=0, so that the first slope straight line changes from a three-dimensional space straight line to a two-dimensional plane straight line, that is, the projection straight line equation obtained by projecting the first slope straight line equation onto the XZ plane is Ax+Cz+D=0. If the position coordinates of the candidate laser scanning point between point q2' and point q1' are (x', y', z'), since the data of the y axis can be ignored, the position coordinates of the candidate laser scanning point between point q2' and point q1' when projected onto the XZ plane are (x', z'), so the height value of the candidate laser scanning point W in the world coordinate system can be calculated according to the point-to-straight line distance formula.

[0118] Preferably, the viewing angle range of the third laser sensor is 70°. To ensure accurate cliff recognition results, during installation, it should be ensured that when the laser scanning point projected by the laser beam emitted by the third laser sensor is projected onto the ground, the distance between the target laser scanning point P and the robot 1 or the intelligent vehicle is not greater than a set value (for example, 0.5m), and the installation height of the third laser sensor on the robot 1 or the intelligent vehicle (the height of the third laser sensor from the ground) is not less than 2 / 3 of the height of the robot 1 or the intelligent vehicle.

[0119] Specifically, this embodiment is an optimized embodiment of the above embodiment, and the parts of this embodiment that are the same as the above embodiment will not be repeated one by one. The present invention installs a three-dimensional laser sensor 2 in front of the robot 1 or the intelligent vehicle. Since the robot 1 or the intelligent vehicle may move to the stairwell or escalator during operation, if the height difference of the steps in front is not detected in time, there is a risk of falling. The present invention uses the laser point cloud data collected by the three-dimensional laser sensor 2 to identify whether there is a cliff (steps, stairs, steep slopes, pits, etc.) in the area to be tested.

[0120] The present invention dynamically sets the limiting conditions for cliff detection according to the state change information of the ground, and eliminates the interference factors that affect cliff detection due to environmental reflection or noise caused by infrared sensors or ultrasonic sensors in the prior art, so as to improve the accuracy of the robot 1 or intelligent vehicle in judging cliffs and reduce the misjudgment rate of cliff detection. The present invention parses the detected laser point cloud data into line segment data for analysis to determine whether there is a cliff in the area to be tested. If there is a cliff, a virtual data wall 4 is marked at the area to be tested in the environmental map according to the position coordinates of the candidate laser scanning points. In this way, obstacles or depressions can be automatically avoided, which improves the obstacle avoidance efficiency of the robot 1 or intelligent vehicle and greatly improves the driving safety of the robot 1 or intelligent vehicle.

[0121] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned program modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program units or modules to complete all or part of the functions described above. The program modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into a processing unit, and the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software program units. In addition, the specific names of the program modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0122] One embodiment of the present invention provides a robot, comprising a processor and a memory, wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, and when the computer program is executed by the processor, the steps of the cliff detection method described in any one or more of the above embodiments are implemented.

[0123] One embodiment of the present invention is an intelligent vehicle, comprising a three-dimensional laser sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program stored in the memory to implement the operations performed by the cliff detection method.

[0124] The intelligent vehicle / robot may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above are merely examples of intelligent vehicles / robots and do not constitute a limitation on intelligent vehicles / robots. They may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the intelligent vehicle / robot may also include an input / output interface, a display device, a network access device, a communication bus, a communication interface, and the like. The communication interface and the communication bus may also include an input / output interface, wherein the processor, the memory, the input / output interface, and the communication interface communicate with each other via the communication bus. The memory stores a computer program, and the processor is used to execute the computer program stored in the memory to implement the cliff detection method in the above-mentioned corresponding method embodiment.

[0125] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0126] The memory may be an internal storage unit of the intelligent vehicle / robot, such as a hard disk or memory of the intelligent vehicle / robot. The memory may also be an external storage device of the intelligent vehicle / robot, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the intelligent vehicle / robot. Furthermore, the memory may include both an internal storage unit and an external storage device of the intelligent vehicle / robot. The memory is used to store the computer program and other programs and data required by the intelligent vehicle / robot. The memory may also be used to temporarily store data that has been output or is to be output.

[0127] The communication bus is a circuit that connects the elements described and implements transmission between these elements. For example, the processor receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing according to the decrypted commands. The memory may include program modules, such as kernels, middleware, application programming interfaces (APIs), and applications. The program modules may be composed of software, firmware, or hardware, or at least two of them. The input / output interface forwards commands or data entered by the user through the input / output interface (such as sensors, keyboards, touch screens). The communication interface connects the intelligent vehicle / robot with other network devices, user devices, and networks. For example, the communication interface can be connected to the network via wired or wireless connection to connect to other external network devices or user devices. Wireless communication may include at least one of the following: wireless fidelity (WiFi), Bluetooth (BT), near field communication technology (NFC), global satellite positioning system (GPS), and cellular communication, etc. Wired communication may include at least one of the following: universal serial bus (USB), high-definition multimedia interface (HDMI), asynchronous transmission standard interface (RS-232), etc. The network may be a telecommunication network and a communication network. The communication network may be a computer network, the Internet, the Internet of Things, or a telephone network. The intelligent vehicle / robot may be connected to the network via a communication interface, and the protocol used by the intelligent vehicle / robot to communicate with other network devices may be supported by at least one of an application, an application programming interface (API), a middleware, a kernel, and a communication interface.

[0128] One embodiment of the present invention provides a storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the operations performed by the above cliff detection method corresponding to the embodiment. For example, the storage medium can be a read-only memory (ROM), a random access memory (RAM), a read-only compact disk (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0129] They can be implemented with program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed with hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0132] In the embodiments provided in the present application, it should be understood that the disclosed intelligent vehicles / robots and methods can be implemented in other ways. For example, the intelligent vehicle / robot embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by sending instructions to related hardware through a computer program. The computer program can be stored in a storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program can be in source code form, object code form, executable file or some intermediate form. The storage medium may include: any entity or device capable of carrying the computer program, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example: in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0136] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0137] It should be noted that the above embodiments can be freely combined as needed. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention.

Claims

1. A cliff detection method, It is characterized in that Includes steps: The laser point cloud data is obtained by scanning the area to be measured by a three-dimensional laser sensor; the laser point cloud data includes several groups of single-frame laser data; Searching for target single-frame laser data from the laser point cloud data; the target single-frame laser data includes a target laser scanning point with a minimum height value in a preset two-dimensional coordinate system; According to the laser center point of the laser point cloud data and the spatial coordinates of the target laser scanning point in the preset two-dimensional coordinate system, the laser scanning points in the target single-frame laser data are grouped to obtain the first and second laser arrays; wherein all the laser scanning points in the first and second laser arrays are enclosed to form the target single-frame laser data; Perform clustering processing on the laser scanning points in the first laser array and the second laser array to obtain the first and second clustering arrays respectively; It is determined whether there is a cliff in the area to be measured according to the target laser scanning point, the laser center point and the first and second cluster arrays.

2. The cliff detection method according to claim 1, It is characterized in that The step of searching for target single-frame laser data from the laser point cloud data comprises the following steps: Obtaining coordinate data of all laser scanning points in the single frame of laser data in a laser coordinate system; The spatial coordinates of each laser scanning point in a preset two-dimensional coordinate system are calculated based on the coordinate data and the conversion matrix; the conversion matrix is ​​the conversion relationship between the laser coordinate system and the preset two-dimensional coordinate system; Compare the height values ​​of all laser scanning points in the preset two-dimensional coordinate system according to the spatial coordinates, and find the target laser scanning point with the smallest height value; The single frame of laser data to which the target laser scanning point belongs is determined as the target single frame of laser data.

3. The cliff detection method according to claim 1, It is characterized in that The clustering of the laser scanning points in the first laser array and the second laser array to obtain the first and second clustered arrays comprises the following steps: Obtaining the position coordinates of each laser scanning point in the first and second laser arrays in the carrier coordinate system; Determine, according to the position coordinates, whether a first distance value between each pair of adjacent laser scanning points in the first and second laser arrays is greater than a preset first distance threshold, and determine whether a height value between each pair of adjacent laser scanning points is greater than a preset height threshold; According to the judgment result, the cluster point pairs corresponding to the first and second laser arrays are found; the cluster point pairs are two adjacent laser scanning points whose first distance value is greater than a preset first distance threshold and whose height value is greater than a preset height threshold; According to the clustering point pairs and the target laser scanning points, the first and second laser arrays are grouped to obtain first and second clustering arrays.

4. The cliff detection method according to claim 3, It is characterized in that The step of judging whether there is a cliff in the test area according to the target laser scanning point, the laser center point and the first and second cluster arrays comprises the following steps: Determine two target cluster points close to the target laser scanning point according to the cluster point pairs corresponding to the first and second laser arrays respectively; According to the position coordinates of the target laser scanning point and the two target clustering points, respectively connecting lines to generate a first slope line segment and a second slope line segment; According to the position coordinates of the target laser scanning point and the two target clustering points, projecting them onto the central plane where the laser center point is located to obtain the first and second projection points; Generate a projected clustered line segment according to the first and second projection points; According to the projected clustered line segment, a candidate laser set located within a preset distance value range below the projected clustered line segment is screened out from the second clustered array; Calculating a second distance value between each laser scanning point in the candidate laser set and the projected clustered line segment, and calculating a third distance value between each laser scanning point in the candidate laser set and any target clustered point; Determine whether there is a candidate laser scanning point whose corresponding second distance value is greater than the second distance threshold, and determine whether there is a candidate laser scanning point whose corresponding third distance value is greater than its own maximum contour diameter; If there is a candidate laser scanning point whose corresponding second distance value is greater than the second distance threshold, and there is a candidate laser scanning point whose corresponding third distance value is greater than the maximum contour diameter, it is determined that there is a cliff in the area to be measured.

5. The cliff detection method according to claim 4, It is characterized in that The method of judging whether there is a cliff in the test area according to the laser data corresponding to the two laser arrays comprises the following steps: If there is a cliff in the area to be measured, a virtual data wall is marked and generated at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning points.

6. The cliff detection method according to claim 5, It is characterized in that The step of marking and generating a virtual data wall at the area to be measured in the environment map according to the position coordinates of the candidate laser scanning points comprises the following steps: Calculate the predicted value according to the height value in the position coordinates of the candidate laser scanning point; According to the position coordinates of the candidate laser scanning points, a virtual data wall with a height equal to the predicted value is marked and generated in the environment map.

7. An intelligent vehicle, It is characterized in that It includes a three-dimensional laser sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is used to execute the computer program stored in the memory to implement the operations performed by the cliff detection method according to any one of claims 1 to 6.

8. A robot, It is characterized in that It includes a three-dimensional laser sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is used to execute the computer program stored in the memory to implement the operations performed by the cliff detection method according to any one of claims 1 to 6.

9. A storage medium, It is characterized in that The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the operation performed by the cliff detection method according to any one of claims 1 to 6.

Images