CN110702120A - Map boundary processing method, system, robot and storage medium - Google Patents
Map boundary processing method, system, robot and storage medium Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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Abstract
The invention discloses a map boundary processing method, a map boundary processing system, a robot and a storage medium, wherein the method comprises the following steps: s1: assigning a cost value to each grid in the grid map to form a cost map containing the cost value of each grid; s2: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids. By using the method, the uncleaned boundary of the grid map can be quickly and efficiently searched. According to the invention, the grid map of the known environment is converted into the cost map, different cost values of different types of grids are based, and the boundary grid of the required area is rapidly searched through comparison of the cost values among the grids, so that the complexity of path planning is reduced.
Description
Technical Field
The invention relates to the technical field of robot navigation and intelligent control, in particular to a map boundary processing method, a map boundary processing system, a robot and a storage medium.
Background
The mobile robot is a popular product in the market in recent years, for example, a sweeping robot, and mainly because of the autonomous sweeping capability of the sweeping robot, the sweeping pressure of a user can be greatly reduced, a basic sanitary environment is maintained through multiple times of light cleaning, and the household environment can be clean and tidy through interval matching manual sweeping. According to scanning measurement, the intelligent sweeping robot has a set of efficient cleaning plan which is very necessary. Products with low cleaning efficiency or low cleaning degree are difficult to technically meet the needs of people. For a sweeping robot, the environment of the sweeping robot is basically unknown, the robot is required to start from an unknown place of the unknown environment to construct a map, the position and the posture of the robot are positioned through repeatedly observed map features (such as corners, columns and the like) in the motion process, and the map is constructed in an incremental mode according to the position of the robot, so that the purposes of positioning and map construction are achieved simultaneously. Commonly used environmental maps can be roughly divided into three types: topological maps, geometric maps, grid maps.
The grid map is a product of digital rasterization of a real map in reality. The environment is decomposed into a series of discrete grids, each grid has a value, the grids contain two basic information of coordinates and whether the grids are obstacles, and probability values occupied by each grid are used for representing the environment information and generally identifying whether the grids are obstacles. Each map grid corresponds to a small area in the actual environment, so that the environment information is reflected, and the robot can easily store the map information. The grid map can describe environment information in detail and is easy to create and maintain, but it is not high in precision in the case that the number of grids divided into environments is small, and when a high-precision grid map is required, maintenance and processing time of the grid map by the robot is exponentially increased due to the increase of the number of grids, so that it is difficult to achieve a real-time effect. However, in a large environment or when the grid cells are divided into smaller cells, the amount of calculation in the grid method increases rapidly, and a large number of memory cells are required, which makes real-time processing by a computer difficult. Therefore, in the case of a grid map with a known environment, when the robot performs a full-coverage cleaning motion, it is necessary to quickly find the boundary of an uncleaned and non-obstacle area on the map.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. To this end, the invention provides a map boundary processing method, a map boundary processing system, a robot and a storage medium.
In a first aspect, an embodiment of the present invention provides a map boundary processing method, where the map is a grid map of a known environment, and each grid in the grid map is divided into an obstacle grid, a covered grid and an uncovered grid according to a type, and the method includes the following steps:
s1: assigning a cost value to each grid in the grid map to form a cost map containing the cost value of each grid;
s2: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
In one possible implementation manner, different cost values are respectively given to the covered grids and the uncovered grids according to types for the barrier grids in the grid map, and a cost map containing the cost values of the grids of the types is formed.
In a possible implementation manner, when the grid map is an RGB map, the cost value of the grid is a color value of the grid, and the cost value is represented by (R, G, B), where the value ranges of R, G, and B are 0 to 255.
In one possible implementation, when the grid map is an RGB map, the cost value of the grid is (R38 + G75 + B15) > >7, where (R38 + G75 + B15) > >7 represents a gray scale value obtained by right-shifting the binary value of the (R38 + G75 + B15) calculation result by seven bits.
In one possible implementation, when the grid map is a gray scale map, the cost value of the grid is a gray scale value of the grid.
By the simplified method, the rapidity of the robot work is really improved by processing the map boundary, the processing can be carried out according to the grid cost value through the calibrated grid map, the robot work effect is effectively improved, the work experience can be improved when the method is particularly applied to the field of mobile robots such as family sweeping robots, and the like, and the cleaning effect is better.
In a possible implementation manner, the cost map is traversed, and if the cost value of the current grid indicates that the current grid is an uncovered grid, and the cost value of the previous grid or the next grid of the current grid is different from the cost value of the current grid, the current grid is determined to be a boundary grid of an uncovered area, and the coordinates of the current grid are saved.
Through the grid map division, even in a large environment or under the condition that the grid method calculation amount is rapidly increased when the grid unit division is finer, the grid map division only needs to process through the calibrated grid without calculating all information of the grid, so that a large number of memory units are not needed, and the real-time processing of a computer is simple and rapid. Therefore, under the condition of a grid map of a known environment, when the robot performs full-coverage cleaning movement, the robot can quickly find the boundary which is not cleaned on the map and is not an obstacle area.
In a possible implementation manner, the cost map is traversed, and if the cost value of the current grid indicates that the current grid is an uncovered grid and the current grid is an edge grid of the cost map, the current grid is determined to be a boundary grid of an uncovered area, and coordinates of the current grid are stored.
In one possible implementation, traversing the cost map includes row-wise traversing and column-wise traversing.
In a second aspect, an embodiment of the present invention provides a map processing system, where the map is a grid map of a known environment, and each grid in the grid map is divided into an obstacle grid, a covered grid and an uncovered grid according to types, and the system includes:
an assignment module: the cost map is used for endowing a cost value to each grid in the grid map, and a cost map containing the cost value of each grid is formed;
a boundary determination module: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
In a third aspect, embodiments of the present invention provide a robot comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, performs the steps of the method as described in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a storage medium storing a computer program, which when executed by the processor implements the steps of the method according to the embodiment of the first aspect.
The invention has the beneficial effects that:
1. according to the invention, the grid map of the known environment is converted into the cost map, the grids are represented by the cost values, and based on different cost values of different types of grids, the boundary grids of the required area are quickly searched by comparing the cost values among the grids, so that the complexity of path planning is reduced.
2. According to the invention, the grids of the grid map are classified into different types, and different cost values are given to the grids of different types, so that the subsequent processing of the map boundary is simplified, the rapidity of the robot work is practically improved, the work experience can be improved when the grid map is particularly applied to the field of mobile robots such as family sweeping robots, and the like, and the cleaning effect is better.
Drawings
Fig. 1 is a flowchart for a map boundary processing method according to an embodiment of the present invention.
FIG. 2(a) is a grid map of a known environment according to one embodiment of the present invention.
Fig. 2(b) is a diagram illustrating the result of the method for map boundary processing according to an embodiment of the present invention.
FIG. 3 is a block diagram of a map processing system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A method, a system, and a robot for map boundary processing according to an embodiment of the present invention are described below with reference to the accompanying drawings.
The grid map of the known environment is stored in a remote server, and the server executes the map boundary processing method; or a grid map of the known environment, stored in a mobile robot that performs tasks, and executed by the robot.
Fig. 1 is a flowchart for a map boundary processing method according to an embodiment of the present invention. As shown in fig. 1, the processing method includes the steps of:
s1: and endowing a cost value to each grid in the grid map to form a cost map containing the cost value of each grid.
In step S1, the grid map is a grid map of a known environment, and each grid in the grid map is divided into an obstacle grid, a covered grid and an uncovered grid according to type. Carrying out type division on grids of a grid map of a known environment; and carrying out cost value calculation on the grids of the classified grid map.
The step S1: assigning a cost value to each grid in the grid map, and forming a cost map including the cost value of each grid, specifically comprising: and respectively endowing different cost values to the covered grids and the uncovered grids according to types to the obstacle grids in the grid map, and forming a cost map containing the cost values of the grids of each type.
In a mobile robot that performs a cleaning task, an obstacle grid and a covered grid correspond to a cleaned grid, and an uncovered grid corresponds to an uncovered grid.
Specifically, in this embodiment, the storage format of the map data of the grid map may be RGB color values or gray scale values.
When the map data of the grid map is stored as the color value, the cost value of the grid in step S1 is expressed in the following two forms:
1) the cost value of the grid is the color value of the grid, and the cost value is represented by (R, G, B), wherein the value ranges of R, G and B are 0-255 respectively.
The cost values are determined according to color values (R, G and B), R, G, B take values in the interval of 0-255 respectively, and the cost values are the same if the color values are the same. Which can be classified by color as: barrier grids, represented in black, with a cost value of (0,0, 0); uncovered grid, expressed in white, with cost value of (255 ); the cost value of the covered grid may be other color values than the barrier grid and the uncovered grid, e.g., the cost value may be (134,189,245); or the cost value of the covered grid can also be marked as barrier grid, marked black, with a cost value of (0,0, 0). Of course, the cost values of the barrier grid, the uncovered grid and the covered grid can also be represented by other cost values, as long as the barrier grid, the uncovered grid and the covered grid can be distinguished, and the method is not limited herein.
2) The cost value of the grid is the gray value of the grid, and the cost value is (R38 + G75 + B15) > >7
In the above formula, (R38 + G75 + B15) > >7 indicates that the binary value of the (R38 + G75 + B15) calculation result is shifted to the right by seven bits and converted to obtain the gradation value. For example, R, G, B has a value of (134,189,245) and its cost value is 179. When the storage format of the map data of the grid map may be a gray scale value, the cost value of the grid in the step S1 may be a gray scale value of the grid map data.
The cost value of the grid map may be represented according to a storage format of the grid map data, and may be a color value or a gray value, which is not limited herein.
According to the embodiment of the invention, the grids of the grid map are subjected to type division, different cost values are given to the grids of different types, the cost map of the grid map is constructed according to the cost values of the grid map, the subsequent processing on the map boundary is simplified, the working rapidity of the robot is practically improved, the processing can be carried out according to the grid cost values through the calibrated grid map, the working effect of the robot is effectively improved, the working experience can be improved when the grid map is particularly applied to the field of mobile robots such as household sweeping robots, and the cleaning effect is better.
S2: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
Specifically, step S2 includes the following steps:
s21: according to the row traversal, the traversal rule is as follows:
and traversing the cost map, if the cost value of the current grid indicates that the current grid is an uncovered grid, and 1) the cost value of the previous grid or the next grid of the current grid is different from the cost value of the current grid, 2) or the current grid is an edge grid of the cost map, and 3) or the coordinate of the next grid is stored, determining that the current grid is a boundary grid of an uncovered area, and storing the coordinate of the current grid.
Specifically, case 1: if the cost value of the current grid is different from the cost value of the previous grid, and the cost value of the current grid indicates that the current grid is an uncovered grid, storing the coordinates of the current grid, and continuously traversing;
case 2: if the cost value of the current grid is different from that of the previous grid, and the cost value of the previous grid indicates that the previous grid is an uncovered grid, the coordinate of the previous grid is saved, and traversal is continued;
case 3: if the cost value of the current grid indicates that the current grid is an uncovered grid and the coordinate of the next grid is already stored, storing the coordinate of the current grid and continuously traversing;
case 4: and if the cost value of the current grid indicates that the current grid is an uncovered grid and the cost value of the next grid also indicates that the next grid is an uncovered grid, not saving any grid coordinate and continuing to traverse.
And completing the traversal of the cost map according to the traversal principle.
S22: following column traversal, the traversal rules are as described above for cases 1-4.
Through the two steps, the coordinates of the boundary grid of the uncovered area containing the uncovered grid are saved.
According to the method and the device for determining the boundary grid of the uncovered area, the cost value map is traversed according to the traversing mode of the embodiment of the invention, the boundary grid of the uncovered area can be determined quickly and accurately, and the calculation efficiency and accuracy are improved. According to the embodiment of the invention, the grid map of the known environment is converted into the cost map, different cost values of different types of grids are based, and the boundary grid of the required area is quickly searched through comparison of the cost values among the grids, so that the complexity of path planning is reduced. Through the grid map division, even in a large environment or under the condition that the grid method calculation amount is rapidly increased when the grid unit division is finer, the calibrated grid is only required to be processed, all information of the grid is not required to be calculated, the representative value is only required to be represented by the color of the grid and the like, a large number of memory units are not required, and the real-time processing of a computer is enabled to be simple and rapid. Therefore, under the condition of a grid map of a known environment, when the robot performs full-coverage cleaning movement, the robot can quickly find the boundary which is not cleaned on the map and is not an obstacle area.
Fig. 2(a) is a grid map of a known environment according to an embodiment of the present invention, and fig. 2(b) is a diagram illustrating the result of a method for map boundary processing according to an embodiment of the present invention. As shown in fig. 2(a) and 2(b), the coordinates of each grid can be represented by the coordinates of the lower right corner or the coordinates of the center point of the grid, the different colors of the grids represent different cost values of the grids, and the same color represents the same cost value. The cost value of the grid that has been cleaned will be modified to black and white to the grid that the robot needs to clean. The method quickly finds the coordinates of the boundary grid of all white areas as in the figure. The specific traversal rules are as follows: traversing the whole map according to rows:
condition 1: the cost value of the current trellis is different from the cost value of the previous trellis.
Condition 2: the current grid is white.
Condition 3: the last grid is white.
Condition 4: neither grid is white.
Condition 5: the current point is a map boundary point.
If the conditions 1 and 2 are met and the coordinate of the current grid is not stored, storing the coordinate point of the current grid and continuously traversing.
If the conditions 1 and 3 are met and the coordinate of the previous grid is not stored, the coordinate point of the previous grid is stored and traversal is continued.
If the conditions 2 and 5 are met and the coordinate of the current grid is not stored, storing the coordinate point of the current grid and continuously traversing.
And if the condition 4 is met, no operation is performed, and the traversal is continued.
Until all the grids are traversed.
Then, traversing the whole map according to the columns, and judging the conditions to be the same as the conditions.
After the above two steps, the coordinates of the boundary points of all the areas needing to be cleaned are saved, and the boundaries of the areas not to be cleaned are formed, such as the grids marked with the numbers 1-21 shown in fig. 2(b), and the grids marked with the numbers 1-21 shown in fig. 2(b) are the boundary points of the areas not to be cleaned of the grid map in fig. 2 (a). Through the grid map division, even in a large-scale environment or under the condition that the grid method calculation amount is rapidly increased when the grid unit division is finer, only the calibrated grid needs to be processed, and all information of the grid does not need to be calculated, so that a large number of memory units are not needed, and the real-time processing of a computer is simple and rapid. Therefore, under the condition of a grid map of a known environment, when the robot performs full-coverage cleaning movement, the robot can quickly find the boundary which is not cleaned on the map and is not an obstacle area.
FIG. 3 is a block diagram of a map processing system according to an embodiment of the present invention. As shown in fig. 3, the map processing system of the present invention is a grid map of a known environment, each grid in the grid map is divided into an obstacle grid, a covered grid and an uncovered grid according to types, and the system includes:
an assignment module: the cost map is used for endowing a cost value to each grid in the grid map, and a cost map containing the cost value of each grid is formed;
a boundary determination module: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
The present invention also provides a storage medium storing a computer program which, when executed by the processor, implements a map boundary processing method as described in the above embodiments.
The invention provides a robot comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, implements a method step of map boundary processing as described in the above embodiments. The mobile robot can be any robot which carries out path planning according to a grid map; in particular to a sweeping robot for executing cleaning tasks.
The robot of this embodiment may have the processing system or the storage medium of the above embodiment, and the map boundary processing method of the above embodiment is executed by the processing system or the storage medium, so as to quickly locate the boundary cleaned by the robot.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. The foregoing describes only a few embodiments of the present invention, which are more specific and detailed, and therefore should not be construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (11)
1. A map boundary processing method, wherein the map is a grid map of a known environment, each grid in the grid map is divided into an obstacle grid, a covered grid and an uncovered grid according to types, and the method is characterized by comprising the following steps:
s1: assigning a cost value to each grid in the grid map to form a cost map containing the cost value of each grid;
s2: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
2. The map boundary processing method according to claim 1, wherein step S1 includes:
and respectively endowing different cost values to the covered grids and the uncovered grids according to types to the obstacle grids in the grid map, and forming a cost map containing the cost values of the grids of each type.
3. The map boundary processing method of claim 2, comprising: when the raster map is an RGB map, the cost value of the raster is the color value of the raster, and the cost value is represented by (R, G, B), where the value ranges of R, G, and B are 0-255 respectively.
4. The map boundary processing method of claim 2, comprising: when the grid map is an RGB map, the cost value of the grid (R38 + G75 + B15) > >7, where (R38 + G75 + B15) > >7 represents a gray scale value obtained by right-shifting the binary value of the (R38 + G75 + B15) calculation result by seven bits.
5. The map boundary processing method of claim 2, comprising: and when the grid map is a gray scale map, the cost value of the grid is the gray scale value of the grid.
6. The map boundary processing method according to claim 1, wherein the step S2 includes: and traversing the cost map, if the cost value of the current grid indicates that the current grid is an uncovered grid, and the cost value of the previous grid or the next grid of the current grid is different from the cost value of the current grid, determining that the current grid is a boundary grid of an uncovered area, and storing the coordinates of the current grid.
7. The map boundary processing method according to claim 6, wherein the step S2 further comprises:
and traversing the cost map, if the cost value of the current grid indicates that the current grid is an uncovered grid and the current grid is an edge grid of the cost map, determining that the current grid is a boundary grid of an uncovered area, and storing the coordinates of the current grid.
8. The method of map boundary processing of claim 1, wherein traversing the cost map comprises a row-wise traversal and a column-wise traversal.
9. A map boundary processing system, the map being a grid map of a known environment, each grid in the grid map being divided into barrier grids, covered grids and uncovered grids by type, the system comprising:
an assignment module: the cost map is used for endowing a cost value to each grid in the grid map, and a cost map containing the cost value of each grid is formed;
a boundary determination module: and traversing the cost map, and determining and storing the boundary grids of the uncovered areas containing the uncovered grids according to the cost values of the grids.
10. A robot characterized by a memory and a processor, the memory storing a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1-8.
11. A storage medium storing a computer program, characterized in that the computer program, when executed by the processor, performs the steps of the method according to any of claims 1-8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110764513A (en) * | 2019-11-26 | 2020-02-07 | 小狗电器互联网科技（北京）股份有限公司 | Map blind area identification method |
| CN111464938A (en) * | 2020-03-30 | 2020-07-28 | 滴图(北京)科技有限公司 | Positioning method, positioning device, electronic equipment and computer readable storage medium |
| CN113110457A (en) * | 2021-04-19 | 2021-07-13 | 杭州视熵科技有限公司 | Autonomous coverage inspection method for intelligent robot in indoor complex dynamic environment |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102138769A (en) * | 2010-01-28 | 2011-08-03 | 深圳先进技术研究院 | Cleaning robot and cleaning method thereby |
| KR101299134B1 (en) * | 2012-03-19 | 2013-08-22 | 한국과학기술연구원 | Searching system and method for moving targets in dynamic environment by cooperation of plural robots |
| CN106979785A (en) * | 2017-03-24 | 2017-07-25 | 北京大学深圳研究生院 | A kind of complete traverse path planing method of multi-robot system |
| CN107390698A (en) * | 2017-08-31 | 2017-11-24 | 珠海市微半导体有限公司 | The benefit of sweeping robot sweeps method and chip |
| CN107831773A (en) * | 2017-11-30 | 2018-03-23 | 深圳市沃特沃德股份有限公司 | The method and sweeping robot that sweeping robot all standing cleans |
| CN107913039A (en) * | 2017-11-17 | 2018-04-17 | 北京奇虎科技有限公司 | Block system of selection, device and robot for clean robot |
| CN108681321A (en) * | 2018-04-10 | 2018-10-19 | 华南理工大学 | A kind of undersea detection method that unmanned boat collaboration is formed into columns |
| CN108898605A (en) * | 2018-07-25 | 2018-11-27 | 电子科技大学 | A kind of grating map dividing method based on figure |
| CN108984781A (en) * | 2018-07-25 | 2018-12-11 | 北京理工大学 | A kind of map edge detection method and device for planning of unmanned vehicle area research |
| CN109298717A (en) * | 2018-11-24 | 2019-02-01 | 珠海市微半导体有限公司 | The cleaning method and chip and Intelligent cleaning robot of intelligent robot |
-
2019
- 2019-11-06 CN CN201911077527.3A patent/CN110702120A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102138769A (en) * | 2010-01-28 | 2011-08-03 | 深圳先进技术研究院 | Cleaning robot and cleaning method thereby |
| KR101299134B1 (en) * | 2012-03-19 | 2013-08-22 | 한국과학기술연구원 | Searching system and method for moving targets in dynamic environment by cooperation of plural robots |
| CN106979785A (en) * | 2017-03-24 | 2017-07-25 | 北京大学深圳研究生院 | A kind of complete traverse path planing method of multi-robot system |
| CN107390698A (en) * | 2017-08-31 | 2017-11-24 | 珠海市微半导体有限公司 | The benefit of sweeping robot sweeps method and chip |
| CN107913039A (en) * | 2017-11-17 | 2018-04-17 | 北京奇虎科技有限公司 | Block system of selection, device and robot for clean robot |
| CN107831773A (en) * | 2017-11-30 | 2018-03-23 | 深圳市沃特沃德股份有限公司 | The method and sweeping robot that sweeping robot all standing cleans |
| CN108681321A (en) * | 2018-04-10 | 2018-10-19 | 华南理工大学 | A kind of undersea detection method that unmanned boat collaboration is formed into columns |
| CN108898605A (en) * | 2018-07-25 | 2018-11-27 | 电子科技大学 | A kind of grating map dividing method based on figure |
| CN108984781A (en) * | 2018-07-25 | 2018-12-11 | 北京理工大学 | A kind of map edge detection method and device for planning of unmanned vehicle area research |
| CN109298717A (en) * | 2018-11-24 | 2019-02-01 | 珠海市微半导体有限公司 | The cleaning method and chip and Intelligent cleaning robot of intelligent robot |
Non-Patent Citations (2)
| Title |
|---|
| 余代俊 等，: "《测量学》", 31 March 2016, 地质出版社 * |
| 苑晶 等，: ""面向移动机器人自定位的无线网络构造算法及实现"", 《仪器仪表学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110764513A (en) * | 2019-11-26 | 2020-02-07 | 小狗电器互联网科技（北京）股份有限公司 | Map blind area identification method |
| CN111464938A (en) * | 2020-03-30 | 2020-07-28 | 滴图(北京)科技有限公司 | Positioning method, positioning device, electronic equipment and computer readable storage medium |
| CN113110457A (en) * | 2021-04-19 | 2021-07-13 | 杭州视熵科技有限公司 | Autonomous coverage inspection method for intelligent robot in indoor complex dynamic environment |
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