Patent Publication Number: US-2023152456-A1

Title: Map scanning system and map scanning method

Description:
CROSS - REFERENCE TO RELATED APPLICATION 
     This application claims priority to China Application Serial Number 202111361138.0, filed Nov. 17, 2021, which is herein incorporated by reference in its entirety. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to a map scanning system and a map scanning method, especially a technology that uses point clouds to locate vehicles. 
     Description of Related Art 
     With the development of technology, vehicle has become an indispensable tool for people. “Autonomous vehicle” is currently one of the most popular research projects. “Autonomous vehicle” requires a location positioning technology to confirm the current location of the vehicle for navigation and real-time control. At present, although there are many types of sensors that can be arranged in vehicles for location positioning, the accuracy of the sensors will be affected by special environments (such as tunnel terrain or heavy rain), or even completely unusable. 
     SUMMARY 
     One aspect of the present disclosure is a map scanning method, comprising the following steps: scanning, by a scanning device, a location area of a vehicle to obtain a location data, wherein the scanning device comprises a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data; comparing, by a processor, the location data with a basic map to obtain a first point cloud area corresponding to the location data in the basic map; comparing the scan point cloud data with the first point cloud area to calculate a matching degree value; and when the matching degree value is less than an update threshold value, updating the first point cloud area according to the scan point cloud data. 
     Another aspect of the present disclosure is a map scanning system, comprising a scanning device, a storage unit and a processor. The scanning device is configured to scan a location area of a vehicle to obtain a location data. The scanning device comprises a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data. The storage unit is configured to store a basic map. The basic map comprises a plurality of point cloud areas. The processor is connected to the scanning device and the storage unit, and is configured to obtain the location data. The processor is configured to compare the location data with the plurality of point cloud areas to obtain a first point cloud area corresponding to the location data in the basic map. The processor is configured to compare the scan point cloud data with the first point cloud area to calculate a matching degree value. When the matching degree value is less than an update threshold value, the processor is configured to update the first point cloud area according to the scan point cloud data. 
     Another aspect of the present disclosure is a map scanning method, comprising the following steps: scanning, by a lidar sensor of a scanning device, a location area of a vehicle to obtain a scan point cloud data; scanning, by an image sensor of a scanning device, the location area of a vehicle to obtain a scan image data; comparing, by a processor, the scan image data with a basic map to obtain a first image area in the basic map; comparing, by the processor, the scan point cloud data with a part of the basic map corresponding to the first image area to obtain a first point cloud area in the basic map; and selectively updating the first point cloud area according to the scan point cloud data. 
     It is to be understood that both the foregoing general description and the following detailed description are by examples, and are intended to provide further explanation of the disclosure as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure can be more fully understood by reading the following detailed description of the embodiment, with reference made to the accompanying drawings as follows: 
         FIG.  1 A  is a schematic diagram of a map scanning system in some embodiments of the present disclosure. 
         FIG.  1 B  is a schematic diagram of a map scanning system in some embodiments of the present disclosure. 
         FIG.  2    is a flowchart illustrating a map scanning method in some embodiments of the present disclosure. 
         FIG.  3    is a schematic diagram of the basic map in some embodiments of the present disclosure. 
         FIG.  4    is a schematic diagram of updating the first point cloud area in some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     For the embodiment below is described in detail with the accompanying drawings, embodiments are not provided to limit the scope of the present disclosure. Moreover, the operation of the described structure is not for limiting the order of implementation. Any device with equivalent functions that is produced from a structure formed by a recombination of elements is all covered by the scope of the present disclosure. Drawings are for the purpose of illustration only, and not plotted in accordance with the original size. 
     It will be understood that when an element is referred to as being “connected to” or “coupled to”, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element to another element is referred to as being “directly connected” or “directly coupled,” there are no intervening elements present. As used herein, the term “and/or” includes an associated listed items or any and all combinations of more. 
       FIG.  1 A  and  FIG.  1 B  are schematic diagrams of a map scanning system in some embodiments of the present disclosure. In this embodiment, the map scanning system  100  is implemented to a server  110  and a vehicle  120 . The server  110  stores a basic map configured to location positioning service. The vehicle  120  connects to the server  110  (e.g., by internet), and is configured to regularly uploads the location data, so that the server  110  determines the current location of the vehicle  120  according to the location data, and provide autonomous driving instructions. However, the application of the present disclosure is not limited to this, in some other embodiments, the server  110  can provide the vehicle  120  navigation instructions, or simply serve as a cloud map for the driver of the vehicle  120  as a reference. 
     The server  110  includes a cloud processor  111  and a storage unit  112 . The cloud processor  111  is configured to transmit command to the vehicle, or provide responds according to the request (e.g., request to provide map or coordinates) uploaded by the vehicle  120 . The storage unit  112  is configured to store the basic map, the definition and details of the basic map will be detailed in the following paragraphs. 
     The vehicle  120  includes a terminal processor  121 , a wireless transmission device  122  and a scanning device  123 . The terminal processor  121  is electrically connected to the wireless transmission device  122  and the scanning device  123 , and is configured to connect to the server  110  by the wireless transmission device  122 . 
     As shown in  FIG.  1 A  and  FIG.  1 B , the scanning device  123  is configured to scan a location area R 100  where the vehicle  120  is located, and store the scan result as a location data. In one embodiment, the scanning device  123  includes a lidar sensor  123   a  (i.e., light detection and ranging, LiDAR). The principle of the lidar sensor  123   a  is to transmit a pulsed laser signal, and calculate the distance between the vehicle and the object (e.g., surrounding terrain or buildings) according to the time interval between transmitting the signal and receiving the response signal. After the lidar sensor  123   a  scans the location area R 100 , the scan point cloud data will be obtained. “Point cloud” is a type of three-dimensional spatial data. Since one skilled in art can understand the operation of the lidar sensor  123   a , it will not be explained here. 
     In some embodiments, the scanning device  123  further includes a image sensor 123b(e.g., camera). The image sensor  123   b  scans the location area R 100  to obtained a scan image data. As shown in  FIG.  1 B , the areas R 110  and the areas R 120  are the scanning range of the lidar sensor  123   a  and the image sensor  123   b  respectively. In particular,  FIG.  1 B  is only a schematic diagram. The scanning range of the lidar sensor  123   a  and the image sensor  123   b  is not limited to a 360-degree circle, and the scanning range of the lidar sensor  123   a  is not necessarily smaller than scanning range of the image sensor  123   b . The schematic diagram in  FIG.  1 B  is used to express that the scanning device  123  is configured to scan the surrounding area of the vehicle  120 . 
     The map scanning system  100  of the present disclosure accurately locates the location of the vehicle  120 , and is further configured to updates the basic map.  FIG.  2    is a flowchart illustrating a map scanning method in some embodiments of the present disclosure. The map scanning system  100  confirems the current location of the vehicle  120  according to step S 201 -S 208 , and selectively updates the basic map. 
     In step S 201 , when the vehicle  120  is driving, the scanning device  123  scans the location area of the vehicle  120  to obtain the location data. As mentioned above, “the location data” can be the scan point cloud data detected by the lidar sensor  123   a , and it can also include the scan image data detected by the image sensor  123   b . 
     In step S 202 , after obtaining the location data, the terminal processor  121  uploads the location data to the server  110  through the wireless transmission device  122 (e.g., via Internet N). The cloud processor  111  of the server  110  compares the location data with the basic map to obtain the first point cloud area CR1 corresponding to the location data in the basic map. The comparison method of the cloud processor  111  and the definition of the basic map are described below. 
       FIG.  3    is a schematic diagram of the basic map in some embodiments of the present disclosure. In some embodiments, the basic map  200  includes multiple image areas IR1-IR4 and multiple point cloud areas CR1, CR2. The image areas IR1-IR4 and the point cloud areas CR1, CR2 are overlap to each other to form the basic map  200 . The size of each image area IR1-IR4 and the size of each point cloud area CR1, CR2 need not be the same. For example, the image areas IR1 and the point cloud areas CR1 overlap. In one embodiment, the image areas are larger than the point cloud areas, so one image area can overlap with multiple point cloud areas. 
     Specifically, the image sensor  123   b  uses ORB(Oriented FAST and Rotated BRIEF) feature extraction detection technology to perform SLAM(Simultaneous Localization and Mapping, SLAM) mapping to generate the scan image data. The lidar sensor  123   a  uses the NDT algorithm (Normal Distribution Transform) for SLAM mapping to generate the scan point cloud data. The scan image data and the scan point cloud data are overlapped to form the basic map. The overlap method can use the ICP (Iterative Closest Point) algorithm, but it is not limited to this. In step S 203 , when the server  110  receives the location data, the cloud processor  111  compares the scan image data in the location data with all the image areas IR1-IR4, so as to searches out one of the image areas IR1-IR4 that corresponds to the scan image data, which is the most similar to the image areas, such as a first image area IR1. 
     in step S 204 , after obtaining the first image area IR1 corresponding to the scan image data, the cloud processor  111  further compares the scan point cloud data with the basic map according to the first image area IR1. In other words, the cloud processor  111  compares the scan point cloud data with a part of the basic map corresponding to the first image area IR1 (i.e., all the point cloud areas in the first image area IR1), and search out the most similar point cloud area, such as the first point cloud area CR1. 
     The above steps S 203 -S 204  compare the “image” first, and then compare the “point cloud”. By sequentially comparing different types of the location data, the areas corresponding to the scan point cloud data in the basic map  200  can be quickly found. However, the present disclosure is not limited to this, in some other embodiments, the location data can only include the scan point cloud data, and the cloud processor  111  can directly compare the scan point cloud data with all the point cloud areas CR1, CR2 to find the most similar point cloud area (the first point cloud area CR1). 
     For example, the vehicle  120  drives through a train station, so the location data uploaded by the scanning device  123  is the point cloud data of the “road in front of the train station”. The basic map  200  records the entire city map where the train station is located, which also includes point clouds such as “roads in front of the railway station”, “roads behind the railway station”, and “city center”. Accordingly, by comparing the location data with the basic map  200 , the cloud processor  111  can determine that the vehicle  120  is currently located on the “road in front of the train station”. In some embodiments, by sequentially comparing the “image” and “point cloud” of the location positioning, the map scanning system can determine the current location of the vehicle  120  without using a GPS device(Global Positioning System). 
     In step S 205 , after searching out the first point cloud area, the cloud processor  111  compares the scan point cloud data with the first point cloud area CR1 to obtain a matching degree value. “The matching degree value” is configured to represent the similarity between the current scan result of the vehicle  120  (the scan point cloud data) and the data stored in the server  110  (the first point cloud area). For example, if the matching degree value between the scan point cloud data and the first point cloud area CR1 is 96%, it means that the two are almost the same. Similarly, if the matching degree value between the scan point cloud data and the first point cloud area CR1 is 85%, it means that there is a significant difference between the two. At this time, the server  110  can selectively update the first point cloud area CR1. 
     in step S 206 , the cloud processor  111  determines whether the matching degree value is less than the matching threshold value (e.g., 75%), if the matching degree value is less than the matching threshold value, it means that the difference between the scan point cloud data and the first point cloud area CR1 is too large, and the location result is incorrect. At this time, back to step S 203  to compare the location data with the basic map  200  again. 
     If the matching degree value is larger than the matching threshold value, in step S 207 , the cloud processor  111  determines whether the matching degree value is less than a update threshold value (e.g., 85%, the update threshold value is different from the matching threshold value). In step S 208 , if the matching degree value is less than the update threshold value, it means that the location area R 100  of the vehicle  120  may have changed (e.g., building changing, road trimming, etc.). At this time, the cloud processor  111  will update the first point cloud area CR1 according to the scan point cloud data. If the matching degree value is larger than the update threshold value, it means that the first point cloud area CR1 does not need to be updated, so back to step S 201  to continue to scan and obtain the location result. 
     In some embodiments, when the cloud processor  111  prepares to update the first point cloud area CR1 according to the scan point cloud data, the cloud processor  111  first records the first point cloud area CR1 as the unupdated area. Then, when the vehicle  120  completes the current trip and is in the idle state, the cloud processor  111  updates the first point cloud area CR1 according to the scan point cloud data. The above “the idle state” means that the vehicle  120  stops continuously uploading the location data, or the server  110  stops comparing the location data and the basic map (i.e., the load of the server  110  is relatively small). 
       FIG.  4    is a schematic diagram of updating the first point cloud area CR1 (or a part of the basic map  200 ) in some embodiments of the present disclosure. If the matching degree value is less than the update threshold value and the update program will be performed, the cloud processor  111  marks a difference area DR in the first point cloud area CR1 that is different from the scan point cloud data on the basic map  200 , and the cloud processor  111  clears the difference area. Then, the cloud processor  111  updates point cloud data of the difference area according to the scan point cloud data. 
     In addition, in some other embodiments, the cloud processor  111  determines whether the basic map  200  has been updated recently. If the basic map  200  has been updated during the update period (e.g., within 10 days), the cloud processor  111  stops determining whether the matching degree value is less than the update threshold value (step S 207 ) to reduce the computing load. Similarly, if the cloud processor  111  determines that the basic map has not been updated during the update period, the cloud processor  111  updates the first point cloud area according to the scan point cloud data. 
     In the foregoing map scanning method, various steps are mainly performed by the cloud processor  111 , but in some other embodiments, the steps of the cloud processor  111  can also be performed by the terminal processor  121  instead. In other words, the terminal processor  121  can connect to the storage unit  112  of the server  110  to obtain the basic map  200 , and compare the location data and the basic map accordingly, or update the basic map. Then, the terminal processor  121  can upload the results of performing various steps to the server  110  to reduce the computing load of the cloud processor  111 . 
     The elements, method steps, or technical features in the foregoing embodiments may be combined with each other, and are not limited to the order of the specification description or the order of the drawings in the present disclosure. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present disclosure without departing from the scope or spirit of the present disclosure. In view of the foregoing, it is intended that the present disclosure cover modifications and variations of this present disclosure provided they fall within the scope of the following claims.