CN116616657A - Obstacle recognition method and system and cleaning robot - Google Patents
Obstacle recognition method and system and cleaning robot Download PDFInfo
- Publication number
- CN116616657A CN116616657A CN202210125532.2A CN202210125532A CN116616657A CN 116616657 A CN116616657 A CN 116616657A CN 202210125532 A CN202210125532 A CN 202210125532A CN 116616657 A CN116616657 A CN 116616657A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- laser
- cloud set
- normal vector
- laser point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004140 cleaning Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000013598 vector Substances 0.000 claims description 161
- 238000001514 detection method Methods 0.000 claims description 42
- 230000007246 mechanism Effects 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 10
- 230000008569 process Effects 0.000 abstract description 9
- 238000013507 mapping Methods 0.000 abstract description 4
- 238000004590 computer program Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 9
- 238000003860 storage Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 241001417527 Pempheridae Species 0.000 description 5
- 238000005094 computer simulation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
- A47L11/4011—Regulation of the cleaning machine by electric means; Control systems and remote control systems therefor
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/24—Floor-sweeping machines, motor-driven
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/28—Floor-scrubbing machines, motor-driven
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
- A47L11/4002—Installations of electric equipment
- A47L11/4008—Arrangements of switches, indicators or the like
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
- A47L11/4061—Steering means; Means for avoiding obstacles; Details related to the place where the driver is accommodated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The application relates to an obstacle recognition method, an obstacle recognition system and a cleaning robot. The method comprises the following steps: acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map; when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set; and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle. The application can solve the problems that the accuracy of the cleaning map is affected because the recognition of the obstacle is error in the process of creating the cleaning map due to the limitation of the mapping technology in the traditional technology.
Description
Technical Field
The application relates to the technical field of cleaning equipment, in particular to a method and a system for identifying obstacles and a cleaning robot.
Background
With the continuous development of automation technology and artificial intelligence technology, various cleaning robots such as floor washing machines and sweeping machines are increasingly widely applied, and great convenience is brought to the life of people. In the conventional technology, in a process of cleaning an area to be cleaned by a cleaning robot such as a sweeper, in order to improve the cleaning efficiency of the cleaning robot, a cleaning map of the area to be cleaned needs to be built for the cleaning robot in advance. However, due to the limitation of the mapping technology, errors occur in the identification of the obstacle in the process of creating the cleaning map, and the accuracy of the cleaning map is affected.
Disclosure of Invention
Therefore, the technical problem to be solved by the application is that the conventional technology is limited by the mapping technology, so that errors occur in the identification of the obstacle in the process of creating the cleaning map, and the accuracy of the cleaning map is affected.
In order to solve the technical problem, the application provides an obstacle recognition method applied to a cleaning robot, comprising the following steps:
acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map;
when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set;
and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle.
Optionally, the acquiring a grid map of the area to be cleaned, detecting states of all laser point clouds in the grid map includes:
detecting an area to be cleaned through a laser detection mechanism, and obtaining all laser point clouds of the area to be cleaned; the laser point cloud comprises a plurality of laser detection points;
obtaining a grid map of the area to be cleaned according to all the obtained laser point clouds of the area to be cleaned;
and acquiring states of all laser point clouds in the grid map according to the obtained grid map of the area to be cleaned.
Optionally, when the shape of the point cloud set is detected to be a regular shape, the determining that the point cloud set is an obstacle includes:
when detecting that the shape of the point cloud set is a regular long strip shape, acquiring a matching state of a normal vector of the point cloud set;
and when the matching degree of the normal vector of the point cloud set is detected to be higher, judging that the point cloud set is a wall body.
Optionally, when detecting that the shape of the point cloud set is a regular strip shape, acquiring the matching state of the normal vector of the point cloud set includes:
when detecting that the shape of the point cloud set in the grid map is a regular long strip shape, acquiring normal vectors of the laser point clouds in the point cloud set;
and obtaining the matching state of the normal vectors of the laser point clouds in the point cloud set according to the obtained normal vectors of the laser point clouds.
Optionally, the acquiring the normal vector of each laser point cloud in the point cloud set includes:
according to the obtained point cloud set in the grid map, fitting all the laser point clouds in the point cloud set respectively to obtain corresponding point cloud surfaces;
acquiring a normal vector of the point cloud surface according to the obtained point cloud surface of the laser point cloud;
and obtaining the normal vector of each laser point cloud according to the obtained normal vector of each point cloud surface.
Optionally, the obtaining, according to the obtained normal vector of the laser point cloud, a matching state of the normal vector of the laser point cloud in the point cloud set includes:
detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set according to the obtained normal vector of the laser point clouds in the point cloud set;
when the consistency of the normal vector directions of the laser point clouds is detected to be higher, judging that the matching degree of the normal vectors of the laser point clouds in the point cloud set is higher.
Optionally, after the detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set, the method further includes:
when the consistency of the normal vector direction of part of the laser point clouds with the normal vector directions of other laser point clouds is detected to be low, judging that the matching degree of the normal vector of the laser point clouds in the point cloud set is low;
and deleting the laser point cloud with lower matching degree when the matching degree of the normal vector of the laser point cloud in the point cloud set is lower.
Optionally, after the determining that the point cloud set is a wall body, the method further includes:
judging the category of the wall body according to the normal vector of each laser point cloud of the obtained point cloud set;
when each laser point cloud on two sides of the point cloud set is detected to have normal vectors, and the matching degree of the normal vectors on the two sides is higher, judging that the wall body is an inner wall;
when the fact that each laser point cloud on one side of the point cloud set is detected to have a normal vector and the matching degree of the normal vector is high is detected, the wall body is judged to be an outer wall.
In addition, the application also provides an obstacle recognition system, which is applied to the cleaning robot and comprises:
the system comprises a point cloud state detection module, a point cloud state detection module and a control module, wherein the point cloud state detection module is used for acquiring a grid map of an area to be cleaned and detecting states of all laser point clouds in the grid map;
the point cloud set detection module is in communication connection with the point cloud state detection module and is used for acquiring the state of the point cloud set when a plurality of laser point clouds are detected to be accumulated to form the point cloud set;
and the obstacle judging module is in communication connection with the point cloud set detecting module and is used for judging the point cloud set as an obstacle when the shape of the point cloud set is detected to be a regular shape.
In addition, the application also provides a cleaning robot, which comprises:
a robot body; the method comprises the steps of,
the control processor is arranged on the robot body;
wherein the control processor is configured to:
acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map;
when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set;
and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle.
The technical scheme provided by the application has the following advantages:
according to the obstacle recognition method provided by the application, the grid map of the area to be cleaned is obtained, the state of the laser point clouds in the grid map is detected, and when a plurality of point clouds which are accumulated to form a regular-shape point cloud set are detected, the point cloud set can be judged to be formed by the obstacle, so that the corresponding obstacle at the point cloud set in the area to be cleaned can be judged. In this way, the recognition error of the obstacle in the process of creating the cleaning map of the area to be cleaned can be reduced, and the accuracy of the cleaning map can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of steps of a method for identifying an obstacle according to an embodiment of the present application;
fig. 2 is a schematic diagram Jian Kuangtu of an obstacle identifying system according to an embodiment of the application;
fig. 3 is a schematic view Jian Kuangtu of a cleaning robot according to an embodiment of the present application;
fig. 4 is a schematic perspective view of a cleaning robot according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the application are shown. The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In the present application, unless otherwise indicated, terms of orientation such as "upper, lower, top, bottom" are used generally with respect to the orientation shown in the drawings or with respect to the component itself in the vertical, upright or gravitational direction; also, for ease of understanding and description, "inner and outer" refers to inner and outer relative to the profile of each component itself, but the above-mentioned orientation terms are not intended to limit the present application.
In the conventional technology, in a process of cleaning an area to be cleaned by a cleaning robot such as a sweeper, in order to improve the cleaning efficiency of the cleaning robot, a cleaning map of the area to be cleaned needs to be built for the cleaning robot in advance. However, due to the limitation of the mapping technology, errors occur in the identification of the obstacle in the process of creating the cleaning map, and the accuracy of the cleaning map is affected. In order to solve the technical problems, the application provides an obstacle recognition method, an obstacle recognition system and a cleaning robot.
The obstacle recognition method and system provided by the application can be applied to cleaning robots such as sweeper and other machine equipment (such as vehicles and robots which automatically move in certain areas) which need to recognize the obstacle in the map. In the following embodiments, the present application will be described with reference to a cleaning robot that is applied to a floor sweeper or the like and that needs to recognize an obstacle in a map.
Example 1
The embodiment provides an obstacle recognition method which is applied to a cleaning robot. As shown in fig. 1, the obstacle recognition method may include the steps of:
s100, acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map;
s200, when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set;
and S300, when the shape of the point cloud set is detected to be a regular shape, judging that the point cloud set is an obstacle.
By acquiring the grid map of the area to be cleaned and detecting the state of the laser point clouds in the grid map, when a plurality of point clouds which are accumulated to form a regular-shape point cloud set are detected, the point cloud set can be judged to be formed by the obstacle, and the corresponding obstacle at the point cloud set in the area to be cleaned can be judged. In this way, the recognition error of the obstacle in the process of creating the cleaning map of the area to be cleaned can be reduced, and the accuracy of the cleaning map can be improved.
Specifically, in step S100, a grid map of the area to be cleaned is acquired, and the states of all laser point clouds in the grid map are detected, which may specifically include the steps of:
s110, detecting an area to be cleaned through a laser detection mechanism, and obtaining all laser point clouds of the area to be cleaned; wherein the laser point cloud may include a plurality of laser detection points;
when the cleaning map of the area to be cleaned is created, the area to be cleaned can be detected through a laser detection mechanism arranged on the cleaning robot, and laser point cloud information of the area to be cleaned can be obtained. The laser point cloud information is obtained by laser detection of the area to be cleaned through the laser detection mechanism, and is mainly obstacle information in the area to be cleaned. Each laser spot cloud is formed of a plurality of laser spot points, and the laser spot points are formed by laser beams emitted from the laser detection means, and are irradiated onto the spot points formed on the surrounding obstacle of the cleaning robot.
S120, acquiring a grid map of the area to be cleaned according to all the obtained laser point clouds of the area to be cleaned;
and obtaining the grid map of the area to be cleaned according to the laser point cloud information obtained by carrying out laser detection on the obstacle in the area to be cleaned and the ground grid information of the area to be cleaned.
S130, acquiring states of all laser point clouds in the grid map according to the obtained grid map of the area to be cleaned.
After the grid map formed by the laser point clouds is obtained, the state of the laser point clouds in the grid map can be detected again. Specifically, how the laser point clouds are distributed in the grid map and what shape the laser point clouds are can be detected, and the obstacle information represented by the laser point clouds can be determined according to the how the laser point clouds are distributed in the grid map.
In addition, the laser detection mechanism detects a plurality of laser point clouds formed in the region to be cleaned, some laser point clouds are converged to form point clouds and present the outline shape of the obstacle, and some laser point clouds are in a dispersed state and cannot detect the specific shape. Therefore, in step S200, when it is detected that a plurality of laser point clouds are accumulated to form a point cloud set, the state of the point cloud set is acquired, which facilitates detection of whether or not it is an obstacle by the state of the point cloud set.
Also, in step S300, when the shape of the point cloud is detected as a regular shape, the judgment of the point cloud as an obstacle may specifically include the steps of:
s310, when the shape of the point cloud set is detected to be a regular long strip shape, acquiring the matching state of the normal vector of the point cloud set;
and S320, when the matching degree of the normal vector of the point cloud set is detected to be higher, judging that the point cloud set is a wall body.
Upon detecting that a plurality of laser point clouds are accumulated to form a point cloud, it may be preliminarily determined that the point cloud indicates that an obstacle exists at the position. By analyzing the shape and state of the point cloud, the type of obstacle represented by the point cloud can be further detected.
Specifically, when the point cloud set is detected to be distributed in a long strip shape, the point cloud set can be primarily judged to be represented as a wall body. And by detecting the matching state of the normal vector of the point cloud set, whether the point cloud set is a wall body can be further determined. When the normal vector matching degree of the point cloud set is detected to be higher, the point cloud set can be judged to be a wall.
Further, in step S310, when detecting that the shape of the point cloud set is a regular strip shape, the matching state of the normal vector of the point cloud set is obtained, which may specifically include the following steps:
s312, when the point cloud set in the grid map is detected to be in a regular strip shape, acquiring normal vectors of all laser point clouds in the point cloud set;
s314, according to the obtained normal vector of each laser point cloud, obtaining the matching state of the normal vector of the laser point cloud in the point cloud set.
Since the point cloud is formed by accumulating a plurality of laser point clouds, the matching degree of the normal vector of the point cloud is the matching degree of the normal vector of the plurality of laser point clouds. If the degree of matching of the normal vectors of the plurality of laser point clouds is high, it is proved that the degree of matching of the normal vectors of the point clouds is high.
Further, in step S312, the obtaining of the normal vector of each laser point cloud in the point cloud set may specifically include the following steps:
s3122, fitting all laser point clouds in the point cloud set respectively according to the point cloud set in the obtained grid map to obtain a corresponding point cloud surface;
according to the obtained point cloud set in the grid map, fitting can be carried out on laser detection points of all laser point clouds in the point cloud set to obtain corresponding point cloud surfaces. Specifically, a plurality of laser detection points on each laser point cloud can be connected together to form a corresponding point cloud surface in a fitting manner.
S3124, obtaining a normal vector of a point cloud surface according to the obtained point cloud surface of the laser point cloud;
after a point cloud surface formed by fitting a plurality of laser detection points on each laser point cloud is obtained, the normal line of the point cloud surface can be obtained, and the normal vector of the point cloud surface can be obtained.
And S3126, acquiring the normal vector of each laser point cloud according to the obtained normal vector of each point cloud surface. The normal vector of each point cloud surface is the normal vector of the corresponding laser point cloud.
In step S314, the matching state of the normal vector of the laser point clouds in the point cloud set is obtained according to the obtained normal vector of the laser point clouds, and specifically the method may include the following steps:
s3142, detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set according to the normal vector of the laser point clouds in the point cloud set;
after the normal vectors of all the laser point clouds in the point cloud set are obtained, whether the direction of the normal vector of each laser point cloud is the same or approximately the same as the direction of the normal vector of other laser point clouds can be detected, and the consistency condition of the normal vector directions of all the laser point clouds in the point cloud set can be obtained.
S3144, when it is detected that the uniformity of the normal vector directions of the laser point clouds is high, it is determined that the matching degree of the normal vectors of the laser point clouds in the point cloud set is high.
That is, when it is detected that the directions of the normal vectors of all the laser point clouds in the point cloud set are mostly identical (i.e., the same or approximately the same), it can be determined that the matching degree of the normal vectors of each laser point cloud in the point cloud set is higher. For example, when the directions of the normal vectors of the laser point clouds in 70% or more (or 80% or more) of the point cloud set are the same, the matching degree of the normal vectors of the laser point clouds in the point cloud set can be determined to be high.
Further, in step S3142, after detecting the coincidence of the normal vector directions of the respective laser point clouds in the point cloud set, the method may further include the steps of:
s3146, when the consistency of the normal vector direction of part of the laser point clouds with the normal vector directions of other laser point clouds is detected to be low, judging that the matching degree of the normal vector of the laser point clouds in the point cloud set is low;
that is, when most of the normal vectors of all the laser point clouds in the point cloud set are not consistent, it can be judged that the matching degree of the normal vectors of all the laser point clouds in the point cloud set is low. For example, when the directions of normal vectors of laser point clouds in 60% or less (or 50% or more) of the point cloud set are the same, the matching degree of the normal vectors of each laser point cloud in the point cloud set can be determined to be low.
S3148, when the matching degree of the normal vector of the laser point clouds in the point cloud set is detected to be lower, deleting the laser point clouds with lower matching degree.
When the fact that the directions of normal vectors of all laser point clouds in the point cloud set are mostly inconsistent is detected, it is proved that errors or mistakes possibly occur in the laser point clouds in the point cloud set, the errors or mistakes cannot be used as a basis for judging obstacles, and the errors or mistakes can be ignored.
In addition, in step S300, after determining that the point cloud set is a wall, the method may further include the following steps:
s400, judging the type of the wall body according to the normal vector of each laser point cloud of the obtained point cloud set;
when the point cloud set is detected to be a wall obstacle, the wall can be further judged to belong to an inner wall or an outer wall. It can be known that the wall body can be divided into an outer wall and an inner wall according to the position and the trend of the wall body in the building. The walls disposed along the peripheral edges of the building may be referred to as exterior walls, and the walls surrounded by the exterior walls may be referred to as interior walls. The inner wall is a wall body which plays a role of separating a space in a room and is not in direct contact with outdoor air.
S500, when each laser point cloud on two sides of the point cloud set is detected to have normal vectors, and the matching degree of the normal vectors on the two sides is higher, judging that the wall body is an inner wall;
when the wall body is the inner wall, both sides of the inner wall can be detected by the laser detection mechanism, and a plurality of laser point clouds can be correspondingly formed on both sides of the inner wall. Therefore, when the corresponding point cloud surfaces are obtained on both sides of the point clouds forming the plurality of laser point clouds of the wall body and the normal vector matching degree of the plurality of laser point clouds on each side is higher, the wall body can be preliminarily judged to be an inner wall. Further, when it is detected that the coincidence of the normal vector of the plurality of laser point clouds on one side of the point cloud and the normal vector of the plurality of laser point clouds on the other side thereof is high, it is determined that the wall is an inner wall (the normal vector directions of both sides of the inner wall are substantially coincident).
And S600, when each laser point cloud on one side of the detected point cloud set has a normal vector, and the matching degree of the normal vector is higher, judging that the wall body is an outer wall.
When the wall body is an outer wall, only the inner side of the outer wall can be detected by the laser detection mechanism, so that a plurality of laser point clouds can be correspondingly formed on only one side of the inner side of the outer wall. And when the inner sides of the point clouds forming the plurality of laser point clouds of the wall body are detected to acquire the corresponding point cloud surfaces, and the normal vector matching degree of the plurality of laser point clouds of the point clouds is higher, judging the wall body as an outer wall.
Example 2
The embodiment provides an obstacle recognition system which is applied to a cleaning robot. As shown in fig. 2, the obstacle recognition system 100 may include:
the point cloud state detection module 102 is configured to obtain a grid map of an area to be cleaned, and detect states of all laser point clouds in the grid map;
the point cloud set detection module 104 is in communication connection with the point cloud state detection module 102, and is used for acquiring the state of the point cloud set when a plurality of laser point clouds are detected to be accumulated to form the point cloud set;
the obstacle determination module 106 is communicatively connected to the point cloud detection module 104, and is configured to determine that the point cloud is an obstacle when the shape of the point cloud is detected to be a regular shape.
Specifically, the point cloud state detection module 102, when configured to obtain a grid map of the area to be cleaned, detects states of all laser point clouds in the grid map, may specifically be configured to:
detecting an area to be cleaned through a laser detection mechanism, and obtaining all laser point clouds of the area to be cleaned; wherein the laser point cloud may include a plurality of laser detection points;
obtaining a grid map of the area to be cleaned according to all the obtained laser point clouds of the area to be cleaned;
and acquiring states of all laser point clouds in the grid map according to the obtained grid map of the area to be cleaned.
Moreover, the obstacle determination module 106 is specifically operable, when determining that the point cloud is an obstacle when detecting that the shape of the point cloud is a regular shape, to:
when detecting that the shape of the point cloud set is a regular long strip shape, acquiring the matching state of the normal vector of the point cloud set;
and when the matching degree of the normal vector of the point cloud set is detected to be higher, judging that the point cloud set is a wall body.
Further, the obstacle determination module 106, when configured to obtain a matching state of the normal vector of the point cloud when detecting that the shape of the point cloud is a regular long stripe, is specifically configured to:
when detecting that the shape of the point cloud set in the grid map is a regular long strip shape, acquiring normal vectors of all laser point clouds in the point cloud set;
and acquiring the matching state of the normal vector of the laser point clouds in the point cloud set according to the obtained normal vector of each laser point cloud.
Moreover, the obstacle determination module 106 is specifically configured to obtain a normal vector of each laser point cloud in the point cloud set:
fitting all laser point clouds in the point cloud set respectively according to the point cloud set in the obtained grid map to obtain a corresponding point cloud surface;
acquiring a normal vector of a point cloud surface according to the point cloud surface of the obtained laser point cloud;
and obtaining the normal vector of each laser point cloud according to the obtained normal vector of each point cloud surface. The normal vector of each point cloud surface is the normal vector of the corresponding laser point cloud.
Moreover, the obstacle determination module 106, when configured to obtain a matching state of the normal vector of the laser point clouds in the point cloud set according to the obtained normal vector of the laser point clouds, is specifically configured to:
according to the obtained normal vector of the laser point clouds in the point cloud set, detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set;
when the consistency of the normal vector directions of the laser point clouds is detected to be higher, the matching degree of the normal vectors of the laser point clouds in the point cloud set is judged to be higher.
Moreover, the obstacle determination module 106, after being configured to detect the coincidence of the normal vector directions of the respective laser point clouds in the point cloud set, is further configured to:
when the consistency of the normal vector direction of part of the laser point clouds and the normal vector directions of other laser point clouds is detected to be low, judging that the matching degree of the normal vector of the laser point clouds in the point clouds is low;
when the matching degree of the normal vector of the laser point clouds in the point cloud set is detected to be lower, deleting the laser point clouds with lower matching degree.
In addition, the obstacle determination module 106, after being configured to determine the point cloud as a wall, may be further configured to:
judging the type of the wall body according to the normal vector of each laser point cloud of the obtained point cloud set;
when each laser point cloud on two sides of the point cloud set is detected to have normal vectors, and the matching degree of the normal vectors on the two sides is higher, judging that the wall body is an inner wall;
when each laser point cloud on one side of the point cloud set is detected to have a normal vector, and the matching degree of the normal vector is higher, the wall body is judged to be an outer wall.
The obstacle recognition system 100 in this embodiment corresponds to the obstacle recognition method described above, and the functions of each module in the obstacle recognition system 100 in this embodiment are described in detail in the corresponding method embodiments, which are not described herein.
Example 3
The present embodiment provides a cleaning robot, as shown in fig. 3 and 4, the cleaning robot 10 may include a robot body 12, and a control processor 14 provided on the robot body 12. The control processor 14 can control the robot body 12 to clean the region to be cleaned, and analyze and identify obstacles in the grid map corresponding to the region to be cleaned.
Moreover, the cleaning robot 10 may include a laser detection mechanism 16 disposed on the robot body 12, the laser detection mechanism 16 being communicatively coupled to the control processor 14. The control processor 14 may control the laser detection mechanism 16 to perform laser detection on the area to be cleaned to obtain laser point cloud information of the area to be cleaned.
Specifically, the control processor 14 may be configured to: acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map; when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set; and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle.
Further, the control processor, when used for acquiring the grid map of the area to be cleaned and detecting the states of all laser point clouds in the grid map, is specifically applicable to:
detecting an area to be cleaned through a laser detection mechanism, and obtaining all laser point clouds of the area to be cleaned; wherein the laser point cloud may include a plurality of laser detection points;
obtaining a grid map of the area to be cleaned according to all the obtained laser point clouds of the area to be cleaned;
and acquiring states of all laser point clouds in the grid map according to the obtained grid map of the area to be cleaned.
Moreover, the control processor is specifically configured to, when detecting that the shape of the point cloud is a regular shape, determine that the point cloud is an obstacle:
when detecting that the shape of the point cloud set is a regular long strip shape, acquiring the matching state of the normal vector of the point cloud set;
and when the matching degree of the normal vector of the point cloud set is detected to be higher, judging that the point cloud set is a wall body.
Further, the control processor may be specifically configured to, when detecting that the shape of the point cloud set is a regular stripe shape, obtain a matching state of a normal vector of the point cloud set:
when detecting that the shape of the point cloud set in the grid map is a regular long strip shape, acquiring normal vectors of all laser point clouds in the point cloud set;
and acquiring the matching state of the normal vector of the laser point clouds in the point cloud set according to the obtained normal vector of each laser point cloud.
Moreover, the control processor is operable to obtain a normal vector for each of the laser point clouds in the point cloud, and is operable to:
fitting all laser point clouds in the point cloud set respectively according to the point cloud set in the obtained grid map to obtain a corresponding point cloud surface;
acquiring a normal vector of a point cloud surface according to the point cloud surface of the obtained laser point cloud;
and obtaining the normal vector of each laser point cloud according to the obtained normal vector of each point cloud surface. The normal vector of each point cloud surface is the normal vector of the corresponding laser point cloud.
Moreover, when the control processor is used for obtaining the matching state of the normal vector of the laser point clouds in the point cloud set according to the obtained normal vector of the laser point clouds, the control processor is specifically used for:
according to the obtained normal vector of the laser point clouds in the point cloud set, detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set;
when the consistency of the normal vector directions of the laser point clouds is detected to be higher, the matching degree of the normal vectors of the laser point clouds in the point cloud set is judged to be higher.
Moreover, the control processor, after being configured to detect a coincidence of the normal vector directions of the respective laser point clouds in the point cloud set, may be further configured to:
when the consistency of the normal vector direction of part of the laser point clouds and the normal vector directions of other laser point clouds is detected to be low, judging that the matching degree of the normal vector of the laser point clouds in the point clouds is low;
when the matching degree of the normal vector of the laser point clouds in the point cloud set is detected to be lower, deleting the laser point clouds with lower matching degree.
In addition, after being used for judging the point cloud set as the wall body, the control processor can be further used for:
judging the type of the wall body according to the normal vector of each laser point cloud of the obtained point cloud set;
when each laser point cloud on two sides of the point cloud set is detected to have normal vectors, and the matching degree of the normal vectors on the two sides is higher, judging that the wall body is an inner wall;
when each laser point cloud on one side of the point cloud set is detected to have a normal vector, and the matching degree of the normal vector is higher, the wall body is judged to be an outer wall.
Similarly, in this embodiment, the control processor may be configured to control the cleaning robot to implement each step in the above-mentioned obstacle identifying method, and the specific implementation manner may refer to the specific content of the above-mentioned obstacle identifying method, which is not described herein again.
Also, in the present embodiment, the cleaning robot may be provided as a sweeper. Further, the cleaning robot may be provided as a floor cleaning machine having a laser detection mechanism, an unmanned floor cleaning machine, a dust collector, or the like.
Furthermore, the application proposes a computer-readable storage medium in which computer-executable instructions are stored, which computer-executable instructions, when executed by a processor, are adapted to carry out all or part of the method steps of the obstacle identification method as described above.
The present application may be implemented by implementing all or part of the above-described method flow, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the above-described method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Based on the same inventive concept, the embodiment of the application also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor realizes all or part of the method steps in the obstacle recognition method when executing the computer program.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or models, and the processor implements various functions of the computer device by running or executing the computer programs and/or models stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the handset. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, server, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. An obstacle recognition method applied to a cleaning robot, the method comprising:
acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map;
when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set;
and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle.
2. The obstacle identifying method according to claim 1, wherein the acquiring a grid map of an area to be cleaned, detecting states of all laser point clouds in the grid map, comprises:
detecting an area to be cleaned through a laser detection mechanism, and obtaining all laser point clouds of the area to be cleaned; the laser point cloud comprises a plurality of laser detection points;
obtaining a grid map of the area to be cleaned according to all the obtained laser point clouds of the area to be cleaned;
and acquiring states of all laser point clouds in the grid map according to the obtained grid map of the area to be cleaned.
3. The obstacle identifying method according to claim 1, wherein when the shape of the point cloud is detected as a regular shape, the judging that the point cloud is an obstacle includes:
when detecting that the shape of the point cloud set is a regular long strip shape, acquiring a matching state of a normal vector of the point cloud set;
and when the matching degree of the normal vector of the point cloud set is detected to be higher, judging that the point cloud set is a wall body.
4. The obstacle identifying method according to claim 3, wherein the obtaining the matching state of the normal vector of the point cloud when the shape of the point cloud is detected as a regular elongated shape comprises:
when detecting that the shape of the point cloud set in the grid map is a regular long strip shape, acquiring normal vectors of the laser point clouds in the point cloud set;
and obtaining the matching state of the normal vectors of the laser point clouds in the point cloud set according to the obtained normal vectors of the laser point clouds.
5. The obstacle recognition method of claim 4, wherein the obtaining a normal vector of each laser point cloud in the set of point clouds comprises:
according to the obtained point cloud set in the grid map, fitting all the laser point clouds in the point cloud set respectively to obtain corresponding point cloud surfaces;
acquiring a normal vector of the point cloud surface according to the obtained point cloud surface of the laser point cloud;
and obtaining the normal vector of each laser point cloud according to the obtained normal vector of each point cloud surface.
6. The obstacle identifying method according to claim 4, wherein the obtaining the matching state of the normal vector of the laser point clouds in the point cloud set according to the obtained normal vector of the laser point clouds includes:
detecting the consistency of the normal vector directions of the laser point clouds in the point cloud set according to the obtained normal vector of the laser point clouds in the point cloud set;
when the consistency of the normal vector directions of the laser point clouds is detected to be higher, judging that the matching degree of the normal vectors of the laser point clouds in the point cloud set is higher.
7. The obstacle identifying method according to claim 6, wherein after detecting the coincidence of the normal vector directions of the respective laser point clouds in the point cloud set, further comprising:
when the consistency of the normal vector direction of part of the laser point clouds with the normal vector directions of other laser point clouds is detected to be low, judging that the matching degree of the normal vector of the laser point clouds in the point cloud set is low;
and deleting the laser point cloud with lower matching degree when the matching degree of the normal vector of the laser point cloud in the point cloud set is lower.
8. The method of claim 4, wherein after the determining that the point cloud is a wall, further comprising:
judging the category of the wall body according to the normal vector of each laser point cloud of the obtained point cloud set;
when each laser point cloud on two sides of the point cloud set is detected to have normal vectors, and the matching degree of the normal vectors on the two sides is higher, judging that the wall body is an inner wall;
when the fact that each laser point cloud on one side of the point cloud set is detected to have a normal vector and the matching degree of the normal vector is high is detected, the wall body is judged to be an outer wall.
9. An obstacle recognition system applied to a cleaning robot, comprising:
the system comprises a point cloud state detection module, a point cloud state detection module and a control module, wherein the point cloud state detection module is used for acquiring a grid map of an area to be cleaned and detecting states of all laser point clouds in the grid map;
the point cloud set detection module is in communication connection with the point cloud state detection module and is used for acquiring the state of the point cloud set when a plurality of laser point clouds are detected to be accumulated to form the point cloud set;
and the obstacle judging module is in communication connection with the point cloud set detecting module and is used for judging the point cloud set as an obstacle when the shape of the point cloud set is detected to be a regular shape.
10. A cleaning robot, comprising:
a robot body; the method comprises the steps of,
the control processor is arranged on the robot body;
wherein the control processor is configured to:
acquiring a grid map of an area to be cleaned, and detecting states of all laser point clouds in the grid map;
when a plurality of laser point clouds are detected to be accumulated to form a point cloud set, acquiring the state of the point cloud set;
and when the shape of the point cloud set is detected to be a regular shape, judging the point cloud set as an obstacle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125532.2A CN116616657A (en) | 2022-02-10 | 2022-02-10 | Obstacle recognition method and system and cleaning robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125532.2A CN116616657A (en) | 2022-02-10 | 2022-02-10 | Obstacle recognition method and system and cleaning robot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116616657A true CN116616657A (en) | 2023-08-22 |
Family
ID=87619938
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210125532.2A Pending CN116616657A (en) | 2022-02-10 | 2022-02-10 | Obstacle recognition method and system and cleaning robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116616657A (en) |
-
2022
- 2022-02-10 CN CN202210125532.2A patent/CN116616657A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111797734B (en) | Vehicle point cloud data processing method, device, equipment and storage medium | |
| US8498472B2 (en) | Image-based localization feature point registration apparatus, method and computer-readable medium | |
| CN113907663B (en) | Obstacle map construction method, cleaning robot, and storage medium | |
| CN114942638A (en) | Robot working area map construction method and device | |
| CN110587597B (en) | SLAM closed loop detection method and detection system based on laser radar | |
| JP2023500994A (en) | Obstacle recognition method, device, autonomous mobile device and storage medium | |
| JPH10240944A (en) | High speed face division method for distance data for movable robot | |
| CN110991215B (en) | Lane line detection method and device, storage medium and electronic equipment | |
| CN111026136A (en) | Port unmanned sweeper intelligent scheduling method and device based on monitoring equipment | |
| WO2024087962A1 (en) | Truck bed orientation recognition system and method, and electronic device and storage medium | |
| CN113633221A (en) | Method, device and system for processing missed-scanning area of automatic cleaning equipment | |
| CN112269386A (en) | Method and device for repositioning symmetric environment and robot | |
| CN110084825B (en) | Image edge information navigation-based method and system | |
| CN116616657A (en) | Obstacle recognition method and system and cleaning robot | |
| CN113282088A (en) | Unmanned driving method, device and equipment of engineering vehicle, storage medium and engineering vehicle | |
| CN116863102A (en) | Grid cell division method, device and equipment for robot | |
| CN109388131B (en) | Robot attitude control method and system based on angular point feature recognition and robot | |
| CN114005030A (en) | Truth value acquisition method and system of automatic cleaning equipment and automatic cleaning equipment | |
| CN116965745A (en) | Coordinate repositioning method and system and cleaning robot | |
| WO2023185579A1 (en) | Map updating processing method and system, and cleaning robot | |
| CN116700233A (en) | Map deviation correcting method, system and self-mobile device | |
| CN116530894A (en) | Rapid image construction method and system and cleaning robot | |
| CN116473471A (en) | Anti-falling control method and system and automatic cleaning equipment | |
| CN114271737B (en) | Control method and device of welcome type sweeper | |
| US20220270371A1 (en) | Determining Distance of Objects |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |