Mobile air conditioner navigation method and device, mobile air conditioner and storage medium

Abstract

The application relates to the technical field of mobile air conditioners, and discloses a mobile air conditioner navigation method and device, a mobile air conditioner and a storage medium, wherein a laser radar and a 3D image sensor are arranged on the mobile air conditioner; compared with the prior art which only performs autonomous navigation according to the data collected by the laser radar, the application not only uses the data collected by the laser radar, but also uses the data collected by the 3D image sensor; first and second local grid maps are respectively generated according to the two kinds of data; the two kinds of grid maps are combined to perform mobile air conditioner navigation; the collected data is more comprehensive; the situation that an object higher or lower than the plane of the laser radar cannot be avoided during autonomous navigation of the mobile air conditioner is avoided; the navigation effect is better; and the autonomous navigation capability of the mobile air conditioner is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile air conditioners, and in particular to a mobile air conditioner navigation method and device, a mobile air conditioner and a storage medium. BACKGROUND

[0002] At present, mapping and navigation of mobile robots in household scenarios mostly use 2D laser radars, and cannot obtain rich three-dimensional information of actual scenes, and cannot avoid objects higher or lower than the plane of the laser radar in autonomous navigation.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a mobile air conditioner navigation method and device, a mobile air conditioner and a storage medium, which aims to solve the technical problem that the mobile air conditioner cannot avoid objects higher or lower than the plane of the laser radar in autonomous navigation in the prior art.

[0005] To achieve the above purpose, the present application provides a mobile air conditioner navigation method, which is applied to a mobile air conditioner, and the mobile air conditioner is provided with a laser radar and a 3D image sensor;

[0006] The mobile air conditioner navigation method comprises the following steps:

[0007] Obtaining laser radar data collected by the laser radar and three-dimensional point cloud data collected by the 3D image sensor;

[0008] Generating a first local grid map according to the laser radar data, and generating a second local grid map according to the three-dimensional point cloud data;

[0009] Carrying out mobile air conditioner navigation according to the first local grid map and the second local grid map.

[0010] Optionally, the second local grid map is generated according to the three-dimensional point cloud data, comprising:

[0011] The three-dimensional point cloud data is subjected to downsampling processing to reduce the number of point clouds in the three-dimensional point cloud data, and obtain processed point cloud data;

[0012] The second local grid map is generated according to the processed point cloud data.

[0013] Optionally, the second local grid map is generated according to the processed point cloud data, comprising:

[0014] The processed point cloud data is converted into processed two-dimensional data;

[0015] According to the two-dimensional data to be processed, a second local grid map is generated by a preset mapping algorithm.

[0016] Optionally, the mobile air conditioner navigation according to the first local grid map and the second local grid map comprises:

[0017] The first local grid map and the second local grid map are fused to obtain a fused local grid map.

[0018] A target map is generated according to the fused local grid map.

[0019] The mobile air conditioner navigation is performed based on the target map.

[0020] Optionally, the mobile air conditioner is further provided with a solid-state laser radar.

[0021] The target map is generated according to the fused local grid map, comprising:

[0022] Solid-state laser radar data collected by the solid-state laser radar is obtained.

[0023] A local cost map is generated according to the fused local grid map and the solid-state laser radar data.

[0024] The target map is generated according to the local cost map.

[0025] Optionally, the local cost map is generated according to the fused local grid map and the solid-state laser radar data, comprising:

[0026] A mobile air conditioner body coordinate system corresponding to the body of the mobile air conditioner is established.

[0027] A solid-state laser radar coordinate system corresponding to the solid-state laser radar is established.

[0028] The local cost map is generated according to the mobile air conditioner body coordinate system, the solid-state laser radar coordinate system, the fused local grid map and the solid-state laser radar data.

[0029] Optionally, the local cost map is generated according to the mobile air conditioner body coordinate system, the solid-state laser radar coordinate system, the fused local grid map and the solid-state laser radar data, comprising:

[0030] The solid-state laser radar data is converted to the mobile air conditioner body coordinate system according to the geometric relationship between the mobile air conditioner body coordinate system and the solid-state laser radar coordinate system, to obtain converted solid-state laser radar data.

[0031] A local cost map is generated according to the fused local grid map and the converted solid-state laser radar data.

[0032] In addition, to achieve the above object, the application further provides a mobile air conditioner navigation device, which comprises:

[0033] The data acquisition module is configured to acquire laser radar data collected by a laser radar and three-dimensional point cloud data collected by a 3D image sensor.

[0034] The local map module is configured to generate a first local grid map according to the laser radar data and a second local grid map according to the three-dimensional point cloud data.

[0035] The air conditioner navigation module is configured to perform mobile air conditioner navigation according to the first local grid map and the second local grid map.

[0036] In addition, to achieve the above object, the application further provides a mobile air conditioner, which is provided with a laser radar and a 3D image sensor, and comprises a memory, a processor and a mobile air conditioner navigation program stored in the memory and executable on the processor, wherein the mobile air conditioner navigation program, when executed by the processor, implements the mobile air conditioner navigation method as described above.

[0037] In addition, to achieve the above object, the application further provides a storage medium, which is provided with a mobile air conditioner navigation program, wherein the mobile air conditioner navigation program, when executed by a processor, implements the mobile air conditioner navigation method as described above.

[0038] The mobile air conditioner navigation method provided by the application acquires laser radar data collected by a laser radar and three-dimensional point cloud data collected by a 3D image sensor, generates a first local grid map according to the laser radar data and a second local grid map according to the three-dimensional point cloud data, and performs mobile air conditioner navigation according to the first local grid map and the second local grid map. Compared with the prior art in which autonomous navigation is performed only according to data collected by a laser radar, the application not only uses data collected by a laser radar but also uses data collected by a 3D image sensor, generates a first local grid map and a second local grid map according to the two kinds of data, and performs mobile air conditioner navigation by combining the two kinds of grid maps. The collected data is more comprehensive, and the mobile air conditioner can avoid objects higher or lower than the plane of the laser radar when performing autonomous navigation, so that the navigation effect is better and the autonomous navigation capability of the mobile air conditioner is improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a mobile air conditioner structure schematic diagram of a hardware running environment related to the embodiment scheme of the application.

[0040] Figure 2 Flowchart of the first embodiment of the mobile air conditioner navigation method of the present application;

[0041] Figure 3 Front view of the existing mobile air conditioner of an embodiment of the mobile air conditioner navigation method of the present application;

[0042] Figure 4 Side view of the existing mobile air conditioner of an embodiment of the mobile air conditioner navigation method of the present application;

[0043] Figure 5 Front view of the improved mobile air conditioner of an embodiment of the mobile air conditioner navigation method of the present application;

[0044] Figure 6 Side view of the improved mobile air conditioner of an embodiment of the mobile air conditioner navigation method of the present application;

[0045] Figure 7 Flowchart of the second embodiment of the mobile air conditioner navigation method of the present application;

[0046] Figure 8 Flowchart of the third embodiment of the mobile air conditioner navigation method of the present application;

[0047] Figure 9 Fusion rule representation intention of an embodiment of the mobile air conditioner navigation method of the present application;

[0048] Figure 10 Fusion flowchart of an embodiment of the mobile air conditioner navigation method of the present application;

[0049] Figure 11 Mobile air conditioner body coordinate system and solid-state laser radar coordinate system relationship diagram of an embodiment of the mobile air conditioner navigation method of the present application;

[0050] Figure 12 Functional module diagram of the first embodiment of the mobile air conditioner navigation device of the present application.

[0051] Explanation of reference numerals:

[0052] Reference Name Reference Name 100 Mobile air conditioner 200 Laser radar 300 3D image sensor 400 Solid-state laser radar

[0053] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application.

[0055] Reference Figure 1 , Figure 1This is a schematic diagram of the portable air conditioner structure in the hardware operating environment involved in the embodiments of the present invention.

[0056] like Figure 1 As shown, the portable air conditioner may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and input units such as buttons; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed random access memory (RAM) or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0057] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the portable air conditioner and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0058] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a mobile air conditioner navigation program.

[0059] exist Figure 1 In the portable air conditioner shown, the network interface 1004 is mainly used to connect to the external network and communicate with other network devices; the user interface 1003 is mainly used to connect to the user equipment and communicate with the user equipment; the device of the present invention calls the portable air conditioner navigation program stored in the memory 1005 through the processor 1001 and executes the portable air conditioner navigation method provided in the embodiment of the present invention.

[0060] Based on the above hardware structure, an embodiment of the mobile air conditioner navigation method of the present invention is proposed.

[0061] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the mobile air conditioner navigation method of the present invention.

[0062] In the first embodiment, the mobile air conditioner navigation method is applied to a mobile air conditioner, which is equipped with a lidar and a 3D image sensor.

[0063] The mobile air conditioner navigation method comprises the following steps:

[0064] In step S10, laser radar data collected by a laser radar and three-dimensional point cloud data collected by a 3D image sensor are acquired.

[0065] It should be noted that the execution subject of the embodiment can be a controller of a mobile air conditioner, wherein the mobile air conditioner can be a movable air conditioner, and can also be other devices that can realize the same or similar functions, and the embodiment does not limit this. In the embodiment, the controller of the mobile air conditioner is taken as an example for description.

[0066] It should be noted that, for the consideration of cost and setting demand, the laser radar arranged on the mobile air conditioner used in a general household scene is a 2D laser radar. This kind of laser radar can only detect objects on a plane, and cannot detect objects higher or lower than the plane of the laser radar. Therefore, when the mobile air conditioner autonomously navigates, it cannot avoid objects higher or lower than the plane of the laser radar, which may cause the mobile air conditioner to touch these objects higher or lower than the plane of the laser radar, not only possibly causing damage to the mobile air conditioner, but also possibly damaging the objects in the room, affecting the user's experience.

[0067] In a specific implementation, as shown in Figure 3 and Figure 4 , the figure Figure 3 is a front view of an existing mobile air conditioner, Figure 4 is a side view of the existing mobile air conditioner, and the mobile air conditioner 100 is arranged with a laser radar 300. In the use process, the laser radar 300 can emit light rays ray3 to detect objects on the plane of the laser radar. However, if there are objects higher or lower than ray3, the laser radar cannot detect the objects. For example, Figure 4 object A in the figure is higher than ray3, so ray3 cannot detect object A. In this case, if the mobile air conditioner is controlled to continue driving forward, the mobile air conditioner will collide with object A, possibly causing damage to the mobile air conditioner or object A.

[0068] To solve the above problem, in the embodiment, the mobile air conditioner is arranged with a 3D image sensor in addition to the laser radar. The data collected by the laser radar and the 3D image sensor are fused to perform autonomous navigation of the mobile air conditioner, so that the mobile air conditioner can effectively avoid touching the objects in the room. The 3D image sensor can be a 3D time of flight (TOF) sensor, and can also be other devices that can realize the same or similar functions, and the embodiment does not limit this. In the embodiment, the 3D image sensor is taken as a 3D TOF sensor as an example for description.

[0069] In a specific implementation, as shown in Figure 5 and Figure 6 , Figure 5 is a front view of the improved mobile air conditioner, Figure 6 is a side view of the improved mobile air conditioner, in addition to being provided with a laser radar 300, the mobile air conditioner 100 is also provided with a 3D image sensor 200. Among them, the laser radar 300 and the 3D image sensor 200 can be part of the mobile air conditioner 100, or can be a separate component provided on the surface of the mobile air conditioner 100, and the present embodiment does not limit this. The laser radar 300 and the 3D image sensor 200 can transmit the data collected by each to the controller of the mobile air conditioner 100 through wired connection or wireless connection, and the controller can perform map planning and autonomous navigation. In addition to the above two ways, the laser radar 300 and the 3D image sensor 200 can also transmit the data collected by each to the controller of the mobile air conditioner 100 through other ways, and the present embodiment does not limit this.

[0070] It should be understood that, as shown in Figure 6 , the working principle of the 3D TOF sensor is not the same as that of the laser radar, and there is a difference from the above-mentioned laser radar which can only detect objects on the plane. The 3D TOF sensor detects objects within the range formed by the light ray ray1 and the light ray ray2, so that objects higher or lower than the plane of the laser radar can be detected by the 3D TOF sensor.

[0071] It can be understood that, due to the difference in structure and operating principle between the laser radar and the 3D TOF sensor, the laser radar collects 2D laser radar data, while the 3D TOF sensor collects three-dimensional point cloud data. Therefore, when establishing a map, 2D laser radar data collected by the laser radar and three-dimensional point cloud data collected by the 3D TOF sensor can be obtained respectively.

[0072] Step S20, generating a first local grid map according to the laser radar data, and generating a second local grid map according to the three-dimensional point cloud data.

[0073] It should be understood that for a household scene, the environment is relatively complex, and only using a 2D laser radar cannot fully obtain actual environmental information. The present scheme proposes to use a multi-sensor technology to build a map, which includes a grid map and a cost map. When establishing a grid map, 2D laser radar and 3D TOF sensor data are fused to build a map. The fusion method includes three types, including data layer fusion, feature fusion and decision layer fusion. In the present embodiment, decision layer fusion is adopted, that is, separate mapping and then fusion.

[0074] Therefore, the first local grid map and the second local grid map can be generated respectively according to the lidar data and the three-dimensional point cloud data. The first local grid map is a local grid map generated according to the data collected by the 2D lidar, and the second local grid map is a local grid map generated according to the data collected by the 3D TOF sensor.

[0075] The manner of generating the first local grid map can be that, after obtaining the 2D lidar data collected by the 2D lidar, a grid map can be established using the open source algorithm gmapping on the basis of the obtained 2D lidar data, and the grid map established above is taken as the first local grid map.

[0076] The manner of generating the second local grid map can be that, after obtaining the three-dimensional point cloud data collected by the 3D TOF sensor, the three-dimensional point cloud data can be converted into two-dimensional data to be processed, and a grid map can be established using the open source algorithm gmapping on the basis of the obtained two-dimensional data to be processed, and the grid map established above is taken as the second local grid map.

[0077] In step S30, autonomous navigation of the mobile air conditioner is performed according to the first local grid map and the second local grid map.

[0078] It should be understood that, after the first local grid map and the second local grid map are generated, the two grid maps can be fused to perform autonomous navigation of the mobile air conditioner. Compared with the prior art in which autonomous navigation is performed only according to the data collected by the lidar, the data collected in the embodiment is more comprehensive, the navigation effect is better, and the autonomous navigation capability of the mobile air conditioner is improved.

[0079] It should be understood that, in the actual navigation process of the mobile air conditioner, two kinds of maps are needed. Initially, mapping is performed by the slam algorithm, and a grid map is obtained. The effect of autonomous navigation of the robot using only this grid map is not very good. The grid map (referred to as a static map) can be used as the basis to update the grid map in real time in cooperation with various sensors, and the grid map combined with the sensor data is referred to as a cost map. The cost map is divided into a global cost map and a local cost map. The local cost map needs to be updated at a certain frequency to realize detection of objects suddenly appearing around the mobile air conditioner. The global cost map can be updated once at initialization. The cost in the cost map is divided into three kinds, occupancy, free, and unknown. Among them, only the grid with free cost is passable, and other costs are considered to be not accessible in path planning.

[0080] Therefore, in the embodiment, in order to achieve a better autonomous navigation effect, the first local grid map and the second local grid map can be fused, and then the fused map is updated according to the data of each sensor to obtain a cost map, and navigation is performed based on the obtained cost map. In addition, navigation can also be performed according to the first local grid map and the second local grid map in other ways, which is not limited in the embodiment.

[0081] In the embodiment, compared with the prior art which only performs autonomous navigation according to the data collected by the lidar, the data collected by the 3D image sensor is also used in the embodiment, and the first local grid map and the second local grid map are generated according to the two kinds of data. The two kinds of grid maps are combined to navigate the mobile air conditioner. The collected data is more comprehensive, which avoids the situation that the mobile air conditioner cannot avoid objects higher or lower than the plane of the lidar during autonomous navigation, so that the navigation effect is better, and the autonomous navigation ability of the mobile air conditioner is improved.

[0082] In an embodiment, as shown in Figure 7 The second embodiment of the mobile air conditioner navigation method is proposed based on the first embodiment, and the step S20 includes:

[0083] In step S201, a first local grid map is generated according to the lidar data, and the three-dimensional point cloud data is down-sampled to reduce the number of point clouds in the three-dimensional point cloud data to obtain to-be-processed point cloud data.

[0084] It should be understood that since the data collected by the 3D TOF sensor is not the same as the data collected by the 2D lidar, the three-dimensional point cloud data collected by the 3D TOF sensor cannot be directly mapped like the 2D lidar data, so the three-dimensional point cloud data can be first converted in format, and then the converted data is used for mapping.

[0085] It can be understood that since the three-dimensional point cloud data collected by the 3D TOF contains rich environmental information, in order to avoid too much environmental information affecting mapping, a feature extraction operation can be performed on the three-dimensional point cloud data, and a down-sampling process is performed to reduce the number of point clouds in the three-dimensional point cloud data, and then the processed point cloud data is called to-be-processed point cloud data.

[0086] In step S202, a second local grid map is generated according to the to-be-processed point cloud data.

[0087] It should be noted that after obtaining the to-be-processed point cloud data through the above manner, the point cloud data can be converted into to-be-processed two-dimensional data through a preset conversion algorithm, and then a second local grid map is generated by mapping according to the obtained to-be-processed two-dimensional data through a preset mapping algorithm. The preset conversion algorithm can be an open source algorithm software package pointcloud_to_laserscan, the preset mapping algorithm can be an open source algorithm gmapping, and the to-be-processed two-dimensional data can be data in the same format as 2D laser radar data. In addition, the above algorithm can also be other algorithms that achieve the same function, and the above data can also be other similar data, and the present embodiment does not limit this.

[0088] In a specific implementation, the to-be-processed point cloud data can be converted into to-be-processed two-dimensional data in the same format as 2D laser radar data through the open source algorithm software package pointcloud_to_laserscan, and then a second local grid map is generated by mapping according to the to-be-processed two-dimensional data through the gmapping algorithm.

[0089] In the present embodiment, the number of point clouds in the three-dimensional point cloud data is reduced by downsampling to avoid excessive environmental information affecting mapping, and then the to-be-processed three-dimensional point cloud data is converted to obtain to-be-processed two-dimensional data for user mapping to generate a second local grid map, so that mapping can be performed according to the data collected by the 3D TOF sensor, which is used for subsequent local grid map fusion, improves the comprehensiveness of information detection, and the map generated in this way also makes the autonomous navigation of the mobile air conditioner more practical.

[0090] In an embodiment, as shown in Figure 8 The third embodiment of the navigation method of the mobile air conditioner is based on the first embodiment or the second embodiment, and in the present embodiment, the first embodiment is described. The step S30 comprises:

[0091] Step S301, the first local grid map and the second local grid map are fused to obtain a fused local grid map.

[0092] It should be understood that in the present embodiment, the data collected by the 2D laser radar and the 3D TOF sensor are used to establish grid maps respectively, and then the maps are fused to finally obtain a two-dimensional grid map of the environment, that is, a first local grid map and a second local grid map are created according to laser radar data and three-dimensional point cloud data respectively, and then the two maps are fused to obtain a local grid map of the environment.

[0093] In a specific implementation, as shown in Figure 9 and Figure 10 , the third embodiment of the navigation method of the mobile air conditioner is based on the first embodiment or the second embodiment, and in the present embodiment, the first embodiment is described. The step S30 comprises: Figure 9To express the intent of the fusion rules, it can be based on Figure 9 The fusion rules in the code are used to fuse the first local raster map and the second local raster map. Figure 10 This is a schematic diagram of the fusion process, which can be based on... Figure 10 The process of fusion is used to build maps and fuse them.

[0094] Step S302: Generate the target map based on the fused local raster map.

[0095] It should be understood that after fusing two local grid maps to obtain a fused local grid map, the 2D LiDAR data collected by the 2D LiDAR and the 3D point cloud data collected by the 3D TOF sensor can be combined with the fused local grid map to obtain a local cost map, and then a target map for autonomous navigation can be generated based on the local cost map.

[0096] Furthermore, since both 2D LiDAR and 3D TOF sensors have difficulty accurately detecting low-lying objects in the home environment, a solid-state LiDAR is also installed on the portable air conditioner to detect these low-lying objects. Step S302 includes:

[0097] Acquire solid-state lidar data collected by the solid-state lidar; generate a local cost map based on the fused local grid map and the solid-state lidar data; generate a target map based on the local cost map.

[0098] It should be noted that, as Figure 5 and Figure 6 As shown, a solid-state LiDAR 400 can also be installed on the portable air conditioner 100. The solid-state LiDAR detects low-lying objects using light rays ray 4. The number of solid-state LiDARs 400 can be one, two, or other numbers; this embodiment does not limit this. In this embodiment, two solid-state LiDARs are used as an example. The solid-state LiDAR 400 can be part of the portable air conditioner 100 or a separate component installed on the surface of the portable air conditioner 100; this embodiment does not limit this. The solid-state LiDAR 400 can transmit the collected data to the controller of the portable air conditioner 100 via wired or wireless connection. The controller then performs map planning and autonomous navigation. In addition to the above two methods, the solid-state LiDAR 400 can also transmit the collected data to the controller of the portable air conditioner 100 through other methods; this embodiment does not limit this.

[0099] It should be understood that the cost map is established based on the fused grid map, and the environment information in the entire mobile air conditioner height range needs to be covered since it is used in actual navigation. The 2D laser radar and the 3D TOF sensor have basically covered the environment information in the mobile air conditioner height range, and the low objects on the ground, such as wires, slippers and the like, which are difficult to detect, can be detected by the solid-state laser radar and updated to the local map.

[0100] It should be understood that the cost map needs to be used in actual navigation, that is, the grid cost value in the local map is updated in real time on the static grid map. In the embodiment, the local cost map can be generated according to one or more of the data collected by the three sensors in combination with the fused local grid map. Therefore, in addition to the local cost map which can be generated according to the fused local grid map and the solid-state laser radar data, the data collected by the 2D laser radar and the 3D TOF sensor can also be combined into the local cost map, that is, the local cost map is generated according to the fused local grid map, 2D laser radar data, 3D point cloud data and solid-state laser radar data. The local cost map can also be generated according to the fused local grid map, 2D laser radar data and 3D point cloud data, and the local cost map is updated according to the solid-state laser radar data to obtain an updated local cost map. Other ways can also be used, which are not limited in the embodiment.

[0101] It should be understood that in order to combine the data of the sensors into the local grid map to generate the local cost map, the coordinate system of the three sensor coordinate systems can be established, and then the data of each sensor is converted to the coordinate system in which the fused local grid map is located through coordinate system rotation transformation. Since the principles of establishing the coordinate system and data conversion of the three sensors are the same, in the embodiment, the solid-state laser radar is taken as an example to be described, and the principle of establishing the coordinate system and data conversion of the solid-state laser radar is described below, and the principles of the 2D laser radar and the 3D TOF sensor are the same, which are not described in the embodiment.

[0102] Further, in order to combine the data collected by the solid-state laser radar into the local grid map, the local cost map is generated according to the fused local grid map and the solid-state laser radar data, which includes:

[0103] A mobile air conditioner body coordinate system corresponding to the body of the mobile air conditioner is established; a solid-state laser radar coordinate system corresponding to the solid-state laser radar is established; the solid-state laser radar data is converted to the mobile air conditioner body coordinate system according to the geometric relationship between the mobile air conditioner body coordinate system and the solid-state laser radar coordinate system, to obtain converted solid-state laser radar data; and a local cost map is generated according to the fused local grid map and the converted solid-state laser radar data.

[0104] It should be understood that the first solid-state laser radar can be referred to as solid-state laser radar 1, and the second solid-state laser radar can be referred to as solid-state laser radar 2. The coordinate systems corresponding to the two solid-state laser radars are respectively established. The coordinate system of the solid-state laser radar 1 is gt1, the coordinate system of the solid-state laser radar 2 is gt2, the x direction of the solid-state laser radar is the forward direction of the machine, and the mobile air conditioner body coordinate system is base. The x direction of base is the forward direction of the mobile air conditioner.

[0105] In a specific implementation, as shown in Figure 11 , Figure 11 is a mobile air conditioner body coordinate system and solid-state laser radar coordinate system relationship diagram. In order to be able to establish a two-dimensional grid cost map, the solid-state laser radar data can be converted to the mobile air conditioner body coordinate system, and the process is as follows:

[0106] Suppose the mobile air conditioner body coordinate system (O B X B Y B Z B ), and the coordinate system of the solid-state laser radar (O G X G Y G Z G ). For an obstacle point P in the environment, the coordinates in the solid-state laser radar coordinate system are (X G ,Y G ,Z G ). The geometric relationship between the mobile air conditioner body coordinate system and the solid-state laser radar coordinate system can be expressed as:

[0107]

[0108] Where R is the rotation matrix of the solid-state laser radar to the mobile air conditioner body coordinate system, and T is the translation matrix of the solid-state laser radar to the mobile air conditioner body coordinate system. The range of the local cost map is rotated 2m*2m, and the local cost map is established with the current position of the mobile air conditioner as the origin. The local cost map is updated in real time as the mobile air conditioner navigates. In the updating process, the bresenham algorithm is used to update the idle grid. For the obstacle grid detected by the sensor, the grid cost is assigned as occupied.

[0109] Step S303, moving air conditioner navigation is performed based on the target map.

[0110] It can be understood that after the sensor data is combined into the local grid map to obtain the local cost map, the target map for autonomous navigation can be generated according to the local cost map, and then the autonomous navigation of the mobile air conditioner is performed based on the target map, a cost-effective solution is adopted, and the mobile air conditioner is realized in the full-scene information perception mapping. The low object in the home environment is detected by using the solid-state laser radar to update the cost map, so that the mobile air conditioner can realize autonomous navigation in the complex home environment.

[0111] In the embodiment, the low ground object is detected by the solid-state laser radar, the solid-state laser radar data is combined with the data collected by the 2D laser radar and the 3D TOF sensor, and the combined local grid map is updated to obtain the local cost map, and then the target map is generated for autonomous navigation of the mobile air conditioner, which improves the autonomous navigation capability of the mobile air conditioner and avoids the mobile air conditioner from touching the low object in the home environment.

[0112] In addition, the embodiment of the present application also provides a storage medium, and the storage medium stores a mobile air conditioner navigation program. When the mobile air conditioner navigation program is executed by a processor, the steps of the mobile air conditioner navigation method described above are implemented.

[0113] Since the storage medium adopts all the technical solutions of the above-mentioned embodiments, it at least has all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here.

[0114] In addition, with reference to Figure 12 , the embodiment of the present application also provides a mobile air conditioner navigation device, which comprises:

[0115] The data acquisition module 10 is configured to acquire laser radar data collected by a laser radar and three-dimensional point cloud data collected by a 3D image sensor.

[0116] It should be noted that, considering the cost and setting requirements, the laser radar arranged on the mobile air conditioner used in the general home environment is a 2D laser radar. This kind of laser radar can only detect objects on the plane, and cannot detect objects higher or lower than the plane of the laser radar. Therefore, when the mobile air conditioner is autonomously navigating, it cannot avoid objects higher or lower than the plane of the laser radar, which may cause the mobile air conditioner to touch these objects higher or lower than the plane of the laser radar, not only may cause damage to the mobile air conditioner, but also may damage the objects in the room, affecting the user's experience.

[0117] In specific implementation, as Figure 3 and Figure 4As shown, Figure 3 is a front view of the existing mobile air conditioner, Figure 4 is a side view of the existing mobile air conditioner, the mobile air conditioner 100 is provided with a laser radar 300, in use, the laser radar 300 can emit a light ray ray3, and the object in the plane of the laser radar is detected through the ray3. If there is an object higher or lower than the ray3, the laser radar cannot detect the object, for example, Figure 4 object A in the above is higher than the ray3, so the ray3 cannot detect the object A, in this case, if the mobile air conditioner continues to move forward, the mobile air conditioner will collide with the object A, which may cause damage to the mobile air conditioner or the object A.

[0118] To solve the above problems, in addition to the laser radar, the mobile air conditioner in the embodiment is also provided with a 3D image sensor, the data collected by the laser radar and the 3D image sensor are fused to perform autonomous navigation of the mobile air conditioner, so that the mobile air conditioner can effectively avoid touching the objects in the room. The 3D image sensor can be a 3D time of flight (TOF) sensor, and can also be other devices that can achieve the same or similar functions, and the embodiment does not limit this. In the embodiment, the 3D image sensor is taken as a 3D TOF sensor as an example for description.

[0119] In specific implementation, as shown in Figure 5 and Figure 6 As shown, Figure 5 is a front view of the improved mobile air conditioner, Figure 6 is a side view of the improved mobile air conditioner, in addition to the laser radar 300, the mobile air conditioner 100 is also provided with a 3D image sensor 200. The laser radar 300 and the 3D image sensor 200 can be part of the mobile air conditioner 100, or can be separate components provided on the surface of the mobile air conditioner 100, and the embodiment does not limit this. The laser radar 300 and the 3D image sensor 200 can transmit the data collected by themselves to the controller of the mobile air conditioner 100 through wired connection or wireless connection, and the controller can perform map planning and autonomous navigation. In addition to the above two ways, the laser radar 300 and the 3D image sensor can also transmit the data collected by themselves to the controller of the mobile air conditioner 100 through other ways, and the embodiment does not limit this.

[0120] It should be understood that, as Figure 6As shown, the working principle of the 3D TOF sensor is not the same as that of the laser radar, and there is a difference from the above laser radar which can only detect objects in the plane. The 3D TOF sensor detects objects in the range formed by the light ray ray1 and the light ray ray2, so that objects higher or lower than the plane of the laser radar can be detected by the 3D TOF sensor.

[0121] It can be understood that, due to the difference in structure and working principle between the laser radar and the 3D TOF sensor, the laser radar collects 2D laser radar data, and the 3D TOF sensor collects three-dimensional point cloud data. Therefore, when establishing a map, the 2D laser radar data collected by the laser radar and the three-dimensional point cloud data collected by the 3D TOF sensor can be obtained respectively.

[0122] The local map module 20 is configured to generate a first local grid map according to the laser radar data, and generate a second local grid map according to the three-dimensional point cloud data.

[0123] It should be understood that, for a household scene, the environment is relatively complex, and only using a 2D laser radar cannot fully obtain actual environmental information. The present scheme proposes to use a multi-sensor technology to map, which includes a grid map and a cost map. When establishing a grid map, the data of the 2D laser radar and the 3D TOF sensor are fused to map. The fusion method includes data layer fusion, feature fusion, and decision layer fusion. In this embodiment, decision layer fusion is adopted, that is, mapping is performed respectively and then fused.

[0124] Therefore, the first local grid map and the second local grid map can be generated according to the laser radar data and the three-dimensional point cloud data respectively. The first local grid map is a local grid map generated according to the data collected by the 2D laser radar, and the second local grid map is a local grid map generated according to the data collected by the 3D TOF sensor.

[0125] The method for generating the first local grid map can be that, after obtaining the 2D laser radar data collected by the 2D laser radar, a grid map can be established using the open source algorithm gmapping based on the obtained 2D laser radar data, and the established grid map is taken as the first local grid map.

[0126] The method for generating the second local grid map can be that, after obtaining the three-dimensional point cloud data collected by the 3D TOF sensor, the three-dimensional point cloud data can be converted into to-be-processed two-dimensional data, and a grid map can be established using the open source algorithm gmapping based on the obtained to-be-processed two-dimensional data, and the established grid map is taken as the second local grid map.

[0127] The air conditioner navigation module 30 is configured to navigate the mobile air conditioner according to the first local grid map and the second local grid map.

[0128] It should be understood that after the first local grid map and the second local grid map are generated, the two grid maps can be fused to perform autonomous navigation of the mobile air conditioner. Compared with the prior art which only performs autonomous navigation according to the data collected by the laser radar, the data collected in the embodiment is more comprehensive, the navigation effect is better, and the autonomous navigation capability of the mobile air conditioner is improved.

[0129] It should be understood that the mobile air conditioner needs two kinds of maps in the actual navigation process. The map is obtained by the slam algorithm at the beginning, which is a grid map. The effect of autonomous navigation of the robot using only this map is not very good. The grid map (referred to as a static map) can be used as the basis to update the grid map in real time with a variety of sensors. The map combining the grid map and sensor data is referred to as a cost map. The cost map is divided into a global cost map and a local cost map. The local cost map needs to be updated at a certain frequency to realize the detection of objects suddenly appearing around the mobile air conditioner. The global cost map can be updated once at the initialization time. The cost in the cost map is divided into three types: occupancy, free, and unknown. Among them, only the grid with free cost is passable, and other costs are considered to be not entered when planning the path.

[0130] Therefore, in the embodiment, in order to achieve a better autonomous navigation effect, the first local grid map and the second local grid map can be fused, and then the fused map is updated according to the data of each sensor to obtain a cost map, and navigation is performed based on the obtained cost map. In addition, the first local grid map and the second local grid map can also be navigated in other ways, which are not limited in the embodiment.

[0131] In the embodiment, compared with the prior art which only performs autonomous navigation according to the data collected by the laser radar, the data collected by the laser radar is used in the embodiment, and the data collected by the 3D image sensor is also used. The first local grid map and the second local grid map are generated according to the two kinds of data, and the two grid maps are combined to navigate the mobile air conditioner. The collected data is more comprehensive, which avoids the situation that the mobile air conditioner cannot avoid objects higher or lower than the plane of the laser radar when performing autonomous navigation, so that the navigation effect is better, and the autonomous navigation capability of the mobile air conditioner is improved.

[0132] Other embodiments or specific implementation methods of the mobile air conditioner navigation device described in the present application can refer to the above method embodiments, which will not be described here.

[0133] It should be noted that, in this document, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0134] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0135] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, and of course, they can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software products, and the estimation machine software product is stored in the estimation machine readable storage medium (such as ROM / RAM, magnetic disk, optical disk) mentioned above, including a plurality of instructions for making an intelligent device (which can be a mobile phone, an estimation machine, a mobile air conditioner, or a network mobile air conditioner, etc.) execute the methods described in various embodiments of the present application.

[0136] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation made by using the content of the specification and drawings, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A mobile air conditioner navigation method, characterized in that, The mobile air conditioner navigation method is applied to a mobile air conditioner, which is equipped with a lidar, a 3D image sensor and a solid-state lidar. The mobile air conditioner navigation method includes the following steps: Acquire lidar data collected by lidar and 3D point cloud data collected by 3D image sensor; A first local raster map is generated based on the lidar data, and a second local raster map is generated based on the 3D point cloud data; and Navigating a mobile air conditioner based on the first and second local grid maps involves: fusing the first and second local grid maps; generating a local cost map based on the fused local grid map and solid-state LiDAR data; generating a target map based on the local cost map; and navigating the mobile air conditioner based on the fused map. Specifically, the grid map is used as a base and updated in real time with the help of multiple sensors. The map combining the grid map and sensor data is called the cost map. The cost map is divided into a global cost map and a local cost map. The local cost map is updated at a certain frequency to detect objects that suddenly appear around the mobile air conditioner. The global cost map is updated once during initialization. The costs in the cost map are divided into three types: occupied, idle, and unknown. Only the idle grid is considered passable, while other costs are considered inaccessible during path planning.

2. The mobile air conditioner navigation method as described in claim 1, characterized in that, The step of generating a second local raster map based on the 3D point cloud data includes: The 3D point cloud data is downsampled to reduce the number of points in the 3D point cloud data, resulting in point cloud data to be processed; and A second local raster map is generated based on the point cloud data to be processed.

3. The mobile air conditioner navigation method as described in claim 2, characterized in that, The step of generating a second local raster map based on the point cloud data to be processed includes: Convert the point cloud data to be processed into two-dimensional data to be processed; and Based on the two-dimensional data to be processed, a map is constructed using a preset mapping algorithm to generate a second local raster map.

4. The mobile air conditioner navigation method according to any one of claims 1 to 3, characterized in that, The step of performing mobile air conditioning navigation based on the first local grid map and the second local grid map includes: The first local raster map and the second local raster map are merged to obtain a merged local raster map; Generate the target map based on the merged local raster map; and Mobile air conditioning navigation is performed based on the target map.

5. The mobile air conditioner navigation method as described in claim 4, characterized in that, The portable air conditioner is also equipped with a solid-state lidar. The step of generating the target map based on the fused local raster map includes: Acquire the solid-state lidar data collected by the solid-state lidar; A local cost map is generated based on the fused local grid map and the solid-state lidar data; and Generate a target map based on the local cost map.

6. The mobile air conditioner navigation method as described in claim 5, characterized in that, The step of generating a local cost map based on the fused local grid map and the solid-state lidar data includes: Establish a coordinate system for the portable air conditioner body corresponding to the portable air conditioner body; Establish the solid-state lidar coordinate system corresponding to the solid-state lidar; and A local cost map is generated based on the coordinate system of the mobile air conditioner body, the coordinate system of the solid-state lidar, the fused local grid map, and the solid-state lidar data.

7. The mobile air conditioner navigation method as described in claim 6, characterized in that, The step of generating a local cost map based on the mobile air conditioner's coordinate system, the solid-state lidar coordinate system, the fused local grid map, and the solid-state lidar data includes: Based on the geometric relationship between the coordinate system of the mobile air conditioner and the coordinate system of the solid-state lidar, the solid-state lidar data is transformed into the coordinate system of the mobile air conditioner to obtain the transformed solid-state lidar data; and A local cost map is generated based on the fused local grid map and the converted solid-state LiDAR data.

8. A portable air conditioner navigation device, characterized in that, The portable air conditioner is equipped with a lidar, a 3D image sensor, and a solid-state lidar. The portable air conditioner navigation device includes: The data acquisition module is used to acquire LiDAR data collected by the LiDAR and 3D point cloud data collected by the 3D image sensor. A local map module is used to generate a first local raster map based on the lidar data and a second local raster map based on the three-dimensional point cloud data. The air conditioning navigation module is used to navigate the mobile air conditioner based on the first local grid map and the second local grid map. Specifically, it fuses the first local grid map and the second local grid map, generates a local cost map based on the fused local grid map and solid-state LiDAR data, generates a target map based on the local cost map, and navigates the mobile air conditioner based on the fused map. Specifically, it updates the grid map in real time using multiple sensors as the base. The map combining the grid map and sensor data is called the cost map. The cost map is divided into a global cost map and a local cost map. The local cost map is updated at a certain frequency to detect objects that suddenly appear around the mobile air conditioner. The global cost map is updated once during initialization. The cost in the cost map is divided into three types: occupied, idle, and unknown. Among them, only the idle grid is passable, and other costs are considered unenterable during path planning.

9. A portable air conditioner, characterized in that, The portable air conditioner is equipped with a lidar and a 3D image sensor. The portable air conditioner includes a memory, a processor, and a portable air conditioner navigation program stored in the memory and executable on the processor. When the portable air conditioner navigation program is executed by the processor, it implements the portable air conditioner navigation method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a mobile air conditioner navigation program, which, when executed by a processor, implements the mobile air conditioner navigation method as described in any one of claims 1 to 7.

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