Patent Publication Number: US-2023161356-A1

Title: Method of updating map in fusion slam and robot implementing same

Description:
TECHNICAL FIELD 
     The present disclosure relates to a method of updating a map in fusion SLAM and a robot implementing the same. 
     BACKGROUND 
     A large-scale retail store, a department store, an airport, a golf course, and the like are places where exchange of goods and services takes place between people. Robots may be useful in the places to offer information or convenience to people. 
     Robots may be classified as guide robots, security robots, cleaning robots and the like. The robots move in a space, confirming their positions. 
     The robots are required to hold information on a space, on their current positions, or on a path previously moved by the robots and the like such that the robots move confirming their positions and avoiding obstacles. 
     The robots may store maps to confirm a space and to move in the space. To generate a map, the robots may draw up a map using a variety of sensors, and may match and store various pieces of information in the map. 
     In case a robot uses a plurality of sensors, accuracy may be enhanced in updating a map. Additionally, to enhance accuracy, information sensed by a sensor needs to be verified or its accuracy needs to be determined. 
     Accordingly, in this specification, a method of updating a map using multi sensors is described. 
     DISCLOSURE 
     Technical Problems 
     According to the present disclosure as a means to achieve the above-described objectives, a robot estimates its position on the basis of various types of sensors and updates a map with information acquired by each sensor on the basis of the estimated position, to enhance accuracy of the map. 
     According to the present disclosure, pieces of information stored in a primary map is maintained while a position of a robot is calculated and updated on the basis of information generated by each sensor, to maintain an agreement between data of maps. 
     According to the present disclosure, some of the data acquired by each sensor of a robot during fusion SLAM are used for localization and the other data are used to update a map such that the process of localization and the process of updating a map are simultaneously carried out. 
     Objectives of the present disclosure are not limited to what has been described. Additionally, other objectives and advantages that have not been mentioned may be clearly understood from the following description and may be more clearly understood from embodiments. Further, it will be understood that the objectives and advantages of the present disclosure may be realized via means and a combination thereof that are described in the appended claims. 
     Technical Solutions 
     While a robot that updates a map in fusion SLAM, according to an embodiment, uses two types of sensors, first type information acquired by a first sensor is used to update a first map, and second type information acquired by a second sensor is used to estimate a current position of the robot. 
     For the robot, the first sensor is a LiDAR sensor and the second sensor is a camera sensor, a map storage stores a visual map that is a second map, and a controller loads the visual map to estimate a position of the robot and adds information sensed by the LiDAR sensor to the first map with respect to the estimated position. 
     For the robot, the first sensor is a camera sensor and the second sensor is a LiDAR sensor, the map storage stores a LiDAR map that is the second map, and the controller loads the LiDAR map to estimate a position of the robot and adds information sensed by the camera sensor to the first map with respect to the estimated position. 
     A method of updating a map in fusion SLAM according to an embodiment includes acquiring first type information by a first sensor of a robot, acquiring second type information by a second sensor of the robot, estimating a current position of the robot by a controller of the robot using a second map of a second type stored in a map storage, and updating a first map with the first type information with respect to the estimated position and storing the same in the map storage by the controller. 
     Advantageous Effects 
     According to embodiments of the present disclosure, a robot may estimate its position on the basis of various types of sensors and may update a map using information acquired by each sensor on the basis of the estimated position, thereby making it possible to improve accuracy of the map. 
     According to the embodiments, pieces of information stored in primary maps are maintained while a position of the robot is calculated and updated on the basis of information generated by each sensor, thereby making it possible to maintain an alignment between pieces of information of the maps. 
     According to the embodiments, some pieces of information acquired by each sensor of the robot during fusion SLAM are applied to localization and the other pieces of information are applied to updating the map, thereby making it possible to simultaneously performing localization and update of the map. 
     Effects of the present disclosure are not limited to the above-described ones, and one having ordinary skill in the art to which the disclosure pertains may easily draw various effects from the configuration of the disclosure. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG.  1    shows an appearance of a robot according to an embodiment. 
         FIG.  2    shows components of a control module of a robot according to an embodiment. 
         FIG.  3    shows a process in which a robot moves in a space. 
         FIG.  4    shows a multiple structure of a map according to an embodiment. 
         FIG.  5    shows a process in which a robot according to an embodiment updates a map. 
         FIG.  6    is an updating process according to an embodiment in case a first sensor is a LiDAR sensor and a second sensor is a camera sensor. 
         FIG.  7    shows an updating process according to an embodiment in case a first sensor is a camera sensor and a second sensor is a LiDAR sensor. 
         FIG.  8    shows an optimization process according to an embodiment. 
         FIGS.  9  and  10    show an embodiment in which LiDAR information is added to a map on the basis of a visual map according to an embodiment. 
         FIGS.  11  and  12    show an embodiment in which visual information is added to a map on the basis of a LiDAR map according to an embodiment. 
         FIG.  13    shows a configuration of an AI sever according to an embodiment. 
         FIG.  14    shows a process in which a robot according to an embodiment updates a pose graph on the basis of artificial intelligence during SLAM. 
     
    
    
     DETAILED DESCRIPTIONS 
     Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art to which the present disclosure pertains can easily implement the present disclosure. The present disclosure may be implemented in many different manners and is not limited to the embodiments described herein. 
     In order to clearly illustrate the present disclosure, technical explanation that is not directly related to the present disclosure may be omitted, and same or similar components are denoted by a same reference numeral throughout the specification. Further, some embodiments of the present disclosure will be described in detail with reference to the drawings. In adding reference numerals to components of each drawing, the same components may have the same reference numeral as possible even if they are displayed on different drawings. Further, in describing the present disclosure, a detailed description of related known configurations and functions will be omitted when it is determined that it may obscure the gist of the present disclosure. 
     In describing components of the present disclosure, it is possible to use the terms such as first, second, A, B, (a), (b), and the like. These terms are only intended to distinguish a component from another component, and a nature, an order, a sequence, or the number of the corresponding components is not limited by that term. When a component is described as being “connected,” “coupled” or “connected” to another component, the component may be directly connected or able to be connected to the other component; however, it is also to be understood that an additional component may be “interposed” between the two components, or the two components may be “connected,” “coupled” or “connected” through an additional component. 
     Further, with respect to embodiments of the present disclosure, for convenience of explanation, the present disclosure may be described by subdividing an individual component, but the components of the present disclosure may be implemented within a device or a module, or a component of the present disclosure may be implemented by being divided into a plurality of devices or modules. 
     In this specification, a robot includes devices that are used for specific purposes (cleaning, ensuring security, monitoring, guiding and the like) or that moves offering functions according to features of a space in which the robot is moving, hereunder. Accordingly, in this specification, devices that have transportation means capable of moving using predetermined information and sensors, and that offer predetermined functions are generally referred to as a robot. 
     In this specification, a robot may move with a map stored in it. The map denotes information on fixed objects such as fixed walls, fixed stairs and the like that do not move in a space. Additionally, information on movable obstacles that are disposed periodically, i.e., information on dynamic objects may be stored on the map. 
     As an example, information on obstacles disposed within a certain range with respect to a direction in which the robot moves forward may also be stored in the map. In this case, unlike the map in which the above-described fixed objects are stored, the map includes information on obstacles, which is registered temporarily, and then removes the information after the robot moves. 
     Further, in this specification, the robot may confirm an external dynamic object using various sensors. When the robot moves to a destination in an environment that is crowded with a large number of pedestrians after confirming the external dynamic object, the robot may confirm a state in which waypoints to the destination are occupied by obstacles. 
     Furthermore, the robot may determine the robot arrives at a waypoint on the basis of a degree in a change of directions of the waypoint, and the robot moves to the next waypoint and the robot can move to destination successfully. 
       FIG.  1    shows an appearance of a robot according to an embodiment.  FIG.  1    shows an exemplary appearance. The robot may be implemented as robots having various appearances in addition to the appearance of  FIG.  1   . Specifically, each component may be disposed in different positions in the upward, downward, leftward and rightward directions on the basis of the shape of a robot. 
     A main body  10  may be configured to be long in the up-down direction, and may have the shape of a roly poly toy that gradually becomes slimmer from the lower portion toward the upper portion, as a whole. 
     The main body  10  may include a case  30  that forms the appearance of the robot  1 . The case  30  may include a top cover  31  disposed on the upper side, a first middle cover  32  disposed on the lower side of the top cover  31 , a second middle cover  33  disposed on the lower side of the first middle cover  32 , and a bottom cover  34  disposed on the lower side of the second middle cover  33 . The first middle cover  32  and the second middle cover  33  may constitute a single middle cover. 
     The top cover  31  may be disposed at the uppermost end of the robot  1 , and may have the shape of a hemisphere or a dome. The top cover  31  may be disposed at a height below the average height for adults to readily receive an instruction from a user. Additionally, the top cover  31  may be configured to rotate at a predetermined angle. 
     The robot  1  may further include a control module  150  therein. The control module  150  controls the robot  1  like a type of computer or a type of processor. Accordingly, the control module  150  may be disposed in the robot  1 , may perform functions similar to those of a main processor, and may interact with a user. 
     The control module  150  is disposed in the robot  1  to control the robot during the robot&#39;s movement by sensing objects around the robot. The control module  150  of the robot may be implemented as a software module, a chip in which a software module is implemented as hardware, and the like. 
     A display unit  31   a  that receives an instruction from a user or that outputs information, and sensors, for example, a camera  31   b  and a microphone  31   c  may be disposed on one side of the front surface of the top cover  31 . 
     In addition to the display unit  31   a  of the top cover  31 , a display unit  20  is also disposed on one side of the middle cover  32 . 
     Information may be output by all the two display units  31   a ,  20  or may be output by any one of the two display units  31   a ,  20  according to functions of the robot. 
     Additionally, various obstacle sensors ( 220  in  FIG.  2   ) are disposed on one lateral surface or in the entire lower end portion of the robot  1  like  35   a ,  35   b . As an example, the obstacle sensors include a time-of-flight (TOF) sensor, an ultrasonic sensor, an infrared sensor, a depth sensor, a laser sensor, and a LiDAR sensor and the like. The sensors sense an obstacle outside of the robot  1  in various ways. 
     Additionally, the robot in  FIG.  1    further includes a moving unit that is a component moving the robot in the lower end portion of the robot. The moving unit is a component that moves the robot, like wheels. 
     The shape of the robot in  FIG.  1    is provided as an example. The present disclosure is not limited to the example. Additionally, various cameras and sensors of the robot may also be disposed in various portions of the robot  1 . As an example, the robot of  FIG.  1    may be a guide robot that gives information to a user and moves to a specific spot to guide a user. 
     The Robot in  FIG.  1    may also include a robot that offers cleaning services, security services or functions. The robot may perform a variety of functions. However, in this specification, the focus is on a guide robot for convenience of description. 
     In a state in which a plurality of the robots in  FIG.  1    are disposed in a service space, the robots perform specific functions (guide services, cleaning services, security services and the like). In the process, the robot  1  may store information on its position, may confirm its current position in the entire space, and may generate a path required for moving to a destination. 
       FIG.  2    shows components of a control module of a robot according to an embodiment. 
     The robot may perform both of the functions of generating a map and estimating a position of the robot using the map. 
     Alternately, the robot may only offer the function of generating a map. 
     Alternately, the robot may only offer the function of estimating a position of the robot using the map. Below, the robot of the present disclosure usually offers the function of estimating a position of the robot using the map. Additionally, the robot may offer the function of generating a map or modifying a map. 
     A LiDAR sensor  220  may sense surrounding objects two-dimensionally or three-dimensionally. A two-dimensional LiDAR sensor may sense positions of objects within 360-degree ranges with respect to the robot. LiDAR information sensed in a specific position may constitute a single LiDAR frame. That is, the LiDAR sensor  220  senses a distance between an object disposed outside the robot and the robot to generate a LiDAR frame. 
     As an example, a camera sensor  230  is a regular camera. To overcome viewing angel limitations, two or more camera sensors  230  may be used. An image captured in a specific position constitutes vision information. That is, the camera sensor  230  photographs an object outside the robot and generates a visual frame including vision information. 
     The robot  1 , to which the present disclosure is applied, performs fusion-simultaneous localization and mapping (Fusion-SLAM) using the LiDAR sensor  220  and the camera sensor  230 . 
     In fusion SLAM, LiDAR information and vision information may be combinedly used. The LiDAR information and vision information may be configured as maps. 
     Unlike a robot that uses a single sensor (LiDAR-only SLAM, visual-only SLAM), a robot that uses fusion-SLAM may enhance accuracy of estimating a position. That is, when fusion SLAM is performed by combining the LiDAR information and vision information, map quality may be enhanced. 
     The map quality is a criterion applied to both of the vision map comprised of pieces of vision information, and the LiDAR map comprised of pieces of LiDAR information. At the time of fusion SLAM, map quality of each of the vision map and LiDAR map is enhanced because sensors may share information that is not sufficiently acquired by each of the sensors. 
     Additionally, LiDAR information or vision information may be extracted from a single map and may be used. For example, LiDAR information or vision information, or all the LiDAR information and vision information may be used for localization of the robot in accordance with an amount of memory held by the robot or a calculation capability of a calculation processor, and the like. 
     An interface unit  290  receives information input by a user. The interface unit  290  receives various pieces of information such as a touch, a voice and the like input by the user, and outputs results of the input. Additionally, the interface unit  290  may output a map stored by the robot  1  or may output a course in which the robot moves by overlapping on the map. 
     Further, the interface unit  290  may supply predetermined information to a user. 
     A controller  250  generates a map as in  FIG.  4    that is described below, and on the basis of the map, estimates a position of the robot in the process in which the robot moves. 
     A communication unit  280  may allow the robot  1  to communicate with another robot or an external server and to receive and transmit information. 
     The robot  1  may generate each map using each of the sensors (a LiDAR sensor and a camera sensor), or may generate a single map using each of the sensors and then may generate another map in which details corresponding to a specific sensor are only extracted from the single map. 
     Additionally, the map of the present disclosure may include odometry information on the basis of rotations of wheels. The odometry information is information on distances moved by the robot, which are calculated using frequencies of rotations of a wheel of the robot, or a difference in frequencies of rotations of both wheels of the robot, and the like. The robot may calculate a distance moved by the robot on the basis of the odometry information as well as the information generated using the sensors. 
     The controller  250  in  FIG.  2    may further include an artificial intelligence unit  255  for artificial intelligence work and processing. 
     A plurality of LiDAR sensors  220  and camera sensors  230  may be disposed outside of the robot  1  to identify external objects. 
     In addition to the LiDAR sensor  220  and camera sensor  230  in  FIG.  2   , various types of sensors (a LiDAR sensor, an infrared sensor, an ultrasonic sensor, a depth sensor, an image sensor, a microphone, and the like) are disposed outside of the robot  1 . The controller  250  collects and processes information sensed by the sensors. 
     The artificial intelligence unit  255  may input information that is processed by the LiDAR sensor  220 , the camera sensor  230  and the other sensors, or information that is accumulated and stored while the robot  1  is moving, and the like, and may output results required for the controller  250  to determine an external situation, to process information and to generate a moving path. 
     As an example, the robot  1  may store information on positions of various objects, disposed in a space in which the robot is moving, as a map. The objects include a fixed object such as a wall, a door and the like, and a movable object such as a flower pot, a desk and the like. The artificial intelligence unit  255  may output data on a path taken by the robot, a range of work covered by the robot, and the like, using map information and information supplied by the LiDAR sensor  220 , the camera sensor  230  and the other sensors. 
     Additionally, the artificial intelligence unit  255  may recognize objects disposed around the robot using information supplied by the LiDAR sensor  220 , the camera sensor  230  and the other sensors. The artificial intelligence unit  255  may output meta information on an image by receiving the image. The meta information includes information on the name of an object in an image, a distance between an object and the robot, the sort of an object, whether an object is disposed on a map, and the like. 
     Information supplied by the LiDAR sensor  220 , the camera sensor  230  and the other sensors is input to an input node of a deep learning network of the artificial intelligence unit  255 , and then results are output from an output node of the artificial intelligence unit  255  through information processing of a hidden layer of the deep learning network of the artificial intelligence unit  255 . 
     The controller  250  may calculate a moving path of the robot using date calculated by the artificial intelligence unit  255  or using data processed by various sensors. 
     The robot in  FIG.  2    may perform any one or more of the above-described functions of generating a map and estimating a position while the robot is moving using the map.  FIG.  3    shows an example of a space in which the robot in  FIG.  2    generates a map, or estimates a position using a map. 
       FIG.  3    shows a process in which a robot moves in a space. The robot in the space  40  may move along a line indicated by reference No. 41, and may store information, sensed by the LiDAR sensor in a specific spot, in a map storage  210  using the LiDAR sensor  220 . A basic shape of a space  40  may be stored as a local map. 
     Additionally, the robot may store information sensed by the camera sensor in a specific spot, in the map storage  210  using the camera sensor  230  while the robot is moving in the space  40 . 
     Further, the robot may move in the space of  FIG.  3   , and the robot confirms current position by comparing stored information in the map storage  210 . 
       FIG.  4    shows a multiple structure of a map according to an embodiment.  FIG.  4    shows a double-layer structure in which a backbone is a first layer, and a LiDAR branch and a visual branch are respectively a second layer. The structure as in  FIG.  4    is referred to as a structurally elastic pose graph-based SLAM. 
     The backbone is information on a trajectory of the robot. Additionally, the backbone includes one or more frame nodes corresponding to the trajectory. The frame nodes further include constraint information in a relation between the frame nodes and other frame nodes. An edge between nodes denotes constraint information. The edge denotes odometry constraint information (odometry constraint) or loop constraint information (loop constraint). 
     The LiDAR branch of the second layer is comprised of LiDAR frames. The LiDAR frames include a LiDAR sensing value that is sensed while the robot is moving. At least one or more of the LiDAR frames are set as a LiDAR keyframe. 
     The LiDAR keyframe has a corresponding relation with the nodes of the backbone. In  FIG.  4   , nodes v 1 , v 2 , v 4 , and v 5  of the backbone indicate a LiDAR keyframe among nodes v 1  to v 5  of the backbone. 
     The visual branch of the second layer is comprised of visual keyframes. The visual keyframes indicate one or more visual feature nodes that are camera sensing values (i.e., an image captured by the camera) sensed while the robot is moving. The robot may generate a plurality of visual feature nodes on the basis of the number of camera sensors disposed in the robot. 
     In the map structure of  FIG.  4   , the LiDAR keyframe or the visual keyframe is connected to the frame node of the backbone. Certainly, the LiDAR/visual keyframe may all be connected to a single frame node (v 1 , v 4  and v 5 ). 
     Poses of the robot at the LiDAR or the visual keyframe are same, and the LiDar or the visual keyframe is connected with each frame node. An extrinsic parameter may be added for each keyframe on the basis of a position of the robot, to which the LiDAR sensor or the camera sensor is attached. The extrinsic parameter denotes information on a relative position at which a sensor is attached from the center of the robot. 
     The visual keyframe has a corresponding relation with the node of the backbone. In  FIG.  4   , nodes v 1 , v 3 , v 4 , and v 5  of the backbone indicate a visual keyframe among nodes v 1  to v 5  of the backbone. In  FIG.  2   , a pair of visual feature nodes (visual frames), comprised of two visual feature nodes, denote that the robot  1  captures an image using two camera sensors  230 . There is an increase and a decrease in the number of visual feature nodes in each position on the basis of an increase and a decrease in the number of camera sensors  230 . 
     Edges are displayed between nodes v 1  to v 5  constituting the backbone of the first layer. e 12 , e 23 , e 34 , and e 45  are edges between adjacent nodes, and e 13 , e 35 , and e 25  are edges between non-adjacent nodes. 
     Odometry constraint information, or for short, odometry information denotes constraints between adjacent frame nodes such as e 12 , e 23 , e 34 , and e 45 . Loop constraint information, or for short, loop information denotes constraints between non-adjacent frames such as e 13 , e 25 , and e 35 . 
     The backbone is comprised of a plurality of keyframes. The controller  250  may perform an initial mapping process to add the plurality of keyframes to the backbone. The initial mapping process includes adding the LiDAR keyframe and the visual frame based on the keyframe. 
     The structure of  FIG.  4    is briefly described as follows. The LiDAR branch includes one or more LiDAR frames. The visual branch includes one or more visual frames. 
     Additionally, the backbone includes two or more frame nodes in which any one or more of a LiDAR frame or a visual frame is registered. In this case, the LiDAR frame or the visual frame registered in the frame node is referred to as a keyframe. A pose graph includes the LiDAR branch, the visual branch and the backbone. 
     Further, the pose graph includes odometry information, loop information and the like among frame nodes. The odometry information includes information on rotations, directions, and the like of wheels, which is generated while the robot is moving between frames nodes. The loop information is based on a set of frame nodes connected using specific constraints between visual keyframes around a specific frame node within a maximum sensing distance of the LiDAR sensor  220 . 
     The controller  250  generates the pose graph in  FIG.  4   . The controller  250  stores the LiDAR branch, the visual branch, the backbone, the odometry information between frame nodes, and the pose graph including the premises in the map storage  210 . 
     As described above, the pose graph as in  FIG.  4    may be generated by the robot offering the function of generating a map and may be stored in a map storage  210  of all robots offering the function of driving. 
     The pose graph in  FIG.  4    may be used for estimation of a position by the robot  1  even when any one of the camera sensor  230  and the LiDAR sensor  220  can operate. Alternately, the robot  1  may use another sensor and may enhance accuracy of estimating a position when any one sensor among sensors of the robot has low accuracy or when a plurality of positions are detected in response to information acquired by any one sensor. 
     For example, the robot  1  including one or more camera sensors  230  and one or more LiDAR sensors  220  may estimate a position using information acquired by multi sensors during fusion-SLAM. The robot  1  may estimate a position when any one of the results of estimation of positions that are estimated using each sensor is true. 
     Alternately, the robot  1  may estimate a position using information of each sensor, stored in the map storage  210 , even when only some of the sensors of the robot  1  can operate or even when the robot  1  includes any one sensor. When a single LiDAR sensor  220  is used, the LiDAR sensor  220  covers 360 degrees. 
     The robot  1  may include one or more LiDAR sensors  220  depending on an angle that may be covered by the LiDAR sensor  220 . Further, the robot  1  may include one or more camera sensors  230  to overcome limitations of viewing angles. 
     The robot, as described above, may draw up a map and estimate a position using one or more LiDAR sensors  220  and one or more camera sensors  230 . 
     The robot may carry out two processes when performing SLAM. The robot  1  may carry out a mapping process of drawing up a map that takes the pose graph in  FIG.  4    as an example and a localization process using the map drawn up. Alternately, the robot  1  may carry out any one of the processes. The robot  1  may be classified as a robot that carries out only the mapping process, and a robot that carries out only the localization process. 
     The robot, as in  FIG.  4   , may store the map actually drawn up by the robot. In addition, the robot may store a CAD map on an inside of an existing building. 
     Below, a process of updating the map drawn up for localization by the robot  1  with additional information is described. The controller  250  of the robot  1  may update the map that is previously drawn up for localization and that is stored in the map storage  210  with additional information. 
     The above-described CAD map, which is map information in relation to building design, may differ from information on an actual space where the robot  1  moves. Accordingly, the controller  250  of the robot  1  needs to update the information on the actual space. 
     In case a structure or a sign and the like of a building are changed, the map needs to be updated with information on the changed structure or sign and the like of the building. For example, the controller  250  of the robot  1  may confirm a structural change such as a newly built fake wall, or a removed door and the like, and may update the map with details in relation to the change. An added sign or a changed sign and the like belong to a visual change. The controller  250  of the robot  250  may confirm the visual change and may update the map with details in relation to the change. 
     In this case, the controller  250  may use existing information that is previously stored. For example, the controller  250  maintains an alignment between the existing map and the updated details such that point of interest (POI) information is maintained. 
     That is, the controller  250  does not change a coordinate of the POI during the process of updating a coordinate to maintain the alignment between the added map and the existing map. As an example, the controller  250  does not change a coordinate (100, 100) of a drugstore into a coordinate (120, 120) at the time of updating. 
       FIG.  5    shows a process in which a robot according to an embodiment updates a map. The robot  1  is holding a map as in  FIG.  4    using two types of sensors. The robot may acquire first type information using a first sensor and may perform SLAM. Likewise, the robot may acquire second type information using a second sensor and may perform SLAM. 
     Certainly, information required to perform SLAM, i.e., a map of a space where the robot moves is stored in the map storage  210 . The robot loads the map and performs SLAM. The map storage  210  may store a LiDAR map as a map used for the LiDAR sensor  220  to perform SLAM. Further, the map storage  210  stores a visual map used for the camera sensor  230  to perform SLAM. 
     In  FIG.  5   , the first sensor of the robot senses a surrounding structure and acquires information (S 41 ). 
     For example, in case the first sensor is a LiDAR sensor  220 , sensing data which is acquired by the LiDAR sensor and to which a shape of a two-dimensional or three-dimensional space structure is applied is an example of the information acquired by the first sensor. 
     For example, in case the first sensor is a camera sensor  230 , image information acquired by the camera sensor is an example of the information acquired by the first sensor. 
     The controller  250  of the robot  1  updates the information acquired by the first sensor using a map that is drawn up on the basis of the information generated by the second sensor (S 42 ). That is, the robot performs SLAM using the second sensor. The controller  250  of the robot estimates a current position of the robot using a second map of a second type, and updates a first map with the first type information acquired by the first sensor and stores the same in the map storage  210  with respect to the estimated position. 
     For example, in case the second sensor is a camera sensor  230 , a pose graph (visual pose-graph) comprised of a visual feature node, a visual frame, a visual key frame and the like is an example of a map that is drawn up on the basis of the information generated by the second sensor. 
     For example, in case the first sensor is a LiDAR sensor  220 , a pose graph comprised of a LiDAR frame, a LiDAR key frame and the like is an example of a map that is drawn up on the basis of the information generated by the first sensor. Alternately, a LiDAR-based occupancy grid map is an example of the map. 
       FIG.  6    is an updating process according to an embodiment in case a first sensor is a LiDAR sensor and a second sensor is a camera sensor. The embodiment in  FIG.  6    is referred to as LiDAR partial mapping. 
     The controller  250  of the robot  1  loads a visual map (a visual pose-graph) that is drawn up on the basis of an existing camera sensor  230 , from the map storage  210  (S 51 ). A total collection of visual feature nodes connected with visual branches in the embodiment of  FIG.  4    is as an example of the visual map. 
     As illustrated using the second map of the second type in  FIG.  5   , the controller  250  performs visual SLAM and localizes the robot on the basis of a visual map (S 52 ). This denotes performing visual feature matching by the robot using the loaded visual map. In this case, a 3D-2D algorithm may be applied. 
     In this case, assume that the estimated position of the robot is accurate. The controller  250  of the robot  1  updates an existing map (the first map described with reference to  FIG.  5   ) with information (LiDAR sensing data) sensed by the LiDAR sensor, i.e., with respect to the estimated position of the robot, i.e., in response to the estimated position of the robot (S 53 ). 
     A LiDAR map that is previously drawn up and stored in the map storage  210  by the robot, or a CAD map that is comprised of CAD information is an example of the existing map. In this process, the controller  250  may update LiDAR information while maintaining an alignment with the loaded visual map (S 54 ). 
     For example, the controller  250  additionally stores information (a LiDAR branch or an occupancy grid map) in relation to the LiDAR sensor  220  in the map storage  210  under the assumption that a position confirmed as a result of VISUAL slam through the visual map is accurate. Registering information acquired by the LiDAR sensor  220  additionally on the pose graph in  FIG.  4    is an example of the above-described process. The controller  250  maintains pieces of information that constitute the visual map. Thus, an alignment between the visual map and the first map (a CAD map or a LiDAR map) is maintained. 
     Then, at the time of map optimization or graph optimization, the controller performs optimization only of newly registered LiDAR information without using the existing visual map. This is because the LiDAR information is only changed while the existing visual map is not changed in S 51  to S 54 . 
       FIG.  7    shows an updating process according to an embodiment in case a first sensor is a camera sensor and a second sensor is a LiDAR sensor. The embodiment in  FIG.  7    is referred to as visual partial mapping. 
     The second map of the second type in  FIG.  5    corresponds to a LiDAR map. The controller  250  of the robot  1  loads a LiDAR map (a LiDAR branch or an occupancy grid map) that is drawn up on the basis of an existing LiDAR sensor  220 , from the map storage  210  (S 55 ). A total collection of occupancy grid maps, i.e., local maps, connected to the LiDAR branch in the embodiment of  FIG.  4    is an example of the LiDAR map. 
     The controller  250  estimates a position of the robot  1  while performing LiDAR SLAM on the basis of the LiDAR map that takes an occupancy grid map as an example (S 56 ). In this case, the controller  250  may apply both of the iterative closest point (ICP) and the Monte-Carlo Localization as a method of localization using LiDAR SLAM. 
     According to the ICP method, the robot matches LiDAR scan data generated by the LiDAR sensor  220  of the robot  1  using a previously stored LiDAR map with a stored local map. When a match rate is at a predetermined level or higher, the controller  250  may estimate a current position of the robot. 
     As an example, in case there is a LiDAR map that is drawn up by the robot  1  during the process of localization, the controller  250  performs localization using the ICP because the drawn up LiDAR map is likely to be matched with the stored map. 
     Further, in case the robot  1  uses a CAD map, the CAD map is likely to differ from the stored LiDAR map. Accordingly, the controller  250  performs localization using the MCL. 
     In this case, assume that the estimated position of the robot is accurate. The controller  250  of the robot  1  updates an existing map with information (a visual pose-graph) sensed by the visual sensor  230  with respect to the estimated position of the robot, i.e., in response to the estimated position of the robot (S 57 ). A map that is previously drawn up and stored by the robot or a CAD map is an example of the existing map. 
     In this process, the controller  250  may update visual information while maintaining an alignment with the loaded LiDAR map (S 58 ). 
     For example, the controller  250  additionally stores information (a visual branch or a visual feature node and the like) in relation to the camera sensor  230  as a visual map in the map storage  210  under the assumption that a position confirmed as a result of LiDAR SLAM through the LiDAR map is accurate. 
     Registering information acquired by the camera sensor  230  additionally on the pose graph in  FIG.  4    is an example of the above-described process. The visual map corresponds to the first map that is described with reference to  FIG.  5   . Certainly, the controller  250  may update a CAD map comprised of CAD information. 
     The controller  250  maintains pieces of information that constitute the LiDAR map. Thus, an alignment between the LiDAR map and the first map (a CAD map or a visual map) is maintained. 
     Then, at the time of map optimization or graph optimization, the controller performs optimization only of newly registered visual information without using the existing LiDAR map. This is because the visual information is only changed while the existing LiDAR map is not changed in S 55  to S 58 . 
     To maintain the alignment in S 54  and S 58  of  FIG.  6    and  FIG.  7   , the controller  250  of the robot  1  updates the map with newly added information while maintaining information of the map that is considered a reference. 
     As an example, the controller  250  of the robot  1  determines that a map generated on the basis of another sensor has no error to update a map using information sensed by one sensor. For example, while updating a visual map using the camera sensor, the controller  250  of the robot  1  determines that a LiDAR map stored in the map storage  210  has no error. 
     Likewise, while updating a LiDAR map using the LiDAR sensor, the controller  250  of the robot  1  determines that a visual map stored in the map storage  210  has no error. 
     The robot  1  determines that a map of any one sensor is accurate and performs localization, and performs mapping of sensor information sensed by another sensor. 
     For example, the controller  250  of the robot  1  determines that a LiDAR sensor  220 -based map has no error and performs localization, and updates the map by only applying visual information sensed by the camera sensor  230  to a visual map. In this case, an alignment between the previously stored LiDAR map and the newly updated visual map is maintained. 
     Likewise, the controller  250  of the robot  1  determines that a camera sensor  230 -based map has no error and performs localization, and updates the map by only applying LiDAR information sensed by the LiDAR sensor  220  to a LiDAR map. In this case, an alignment between the previously stored visual map and the newly updated LiDAR map is maintained. 
     Further, optimization may be performed after the update (S 54  and S 58 ) in  FIGS.  6  and  7   . 
       FIG.  8    shows an optimization process according to an embodiment. 
     The controller  250  fixes a map that is maintained by the robot prior to mapping (S 61 ). This process denotes setting the process such that no change is made during the optimization process. Next, the controller  250  does not fix an additionally updated map (S 62 ). This process denotes setting the process such that a change is made during the optimization process. 
     Then the controller  250  performs optimization of parts that are not fixed (S 63 ). Thus, pieces of newly added information are only optimized. 
     For example, described is a case in which partial mapping is performed in a state where maps generated by two types of sensor are all stored in the map storage  210 . As an example, described as follows is application of S 61  to S 63  as an optimization process in case only a visual map is additionally updated in a state where the visual map and a LiDAR map are all stored. 
     In case optimization of a graph is performed after the graph is configured as in  FIG.  4   , the controller  250  fixes a visual map (a vision map) that is previously stored. Additionally, the controller  250  does not fix an additionally updated visual map and performs optimization. 
     For example, in case visual feature nodes of v 1  to v 100  are previously stored information, and v 101  to v 150  are additionally updated information, the controller  250  sets the nodes of v 1  to v 100  not to move. Additionally, the controller  250  sets the nodes of v 101  to v 150  to move and performs optimization. In this case, during the process of optimization, the nodes of v 101  to v 150  are only changed. 
     Thus, the information that is stored previously by the robot  1  may not be changed, and an alignment between the information and a pre-stored map may be maintained even during the process of optimization by the controller  250 . 
     In summary, the controller  250  maintains information of the second map and only optimizes pieces of added information of the first map. 
     In case the above-described embodiments are applied, the robot may improve accuracy by updating other types of maps with respect to a single map. 
       FIGS.  9  and  10    show an embodiment in which LiDAR information is added to a map on the basis of a visual map according to an embodiment. 
     Reference numeral  70  indicates a map stored in the map storage. A visual branch that constitutes a visual map and a LiDAR branch that constitutes a LiDAR map are presented as  70 . The LiDAR map may be optionally included. Alternately, the map storage  210  may store a CAD map rather than the LiDAR map. 
     Accordingly,  70   v  indicates the visual map and  701  indicates the LiDAR map. 
     The controller  250  of the robot  1  performs visual SLAM where image information  71  captured by the camera sensor  230  is compared with images of the visual map ( 70   v ). As a result, as in S 72 , an image that is considered to have the same position by the controller is searched, and the controller determines that a current position of the robot is the frame node of v 5 . 
     In this case, the controller  250  of the robot  1  updates the LiDAR map  701  by associating information  73  sensed by the LiDAR sensor  220  with the position of v 5 . 
     In case the image information  71  captured by the camera sensor  230  is the same as an image registered in the frame node of v 3  and the controller  250  determines that a current position of the robot is the frame node of v 3 , the controller  250  may add a new LiDAR frame to the sensed data of  73 . In this case, like  73   a  in  FIG.  10   , the new LiDAR frame is added to the LiDAR map ( 701 ). 
       FIGS.  11  and  12    show an embodiment in which visual information is added to a map on the basis of a LiDAR map according to an embodiment. 
     Reference numeral  70  indicates a map stored in the map storage. A visual branch that constitutes a visual map and a LiDAR branch that constitutes a LiDAR map are presented as reference numeral  70 . The visual map may be optionally included. Alternately, the map storage  210  may store a CAD map rather than the visual map. 
     Accordingly,  70   v  indicates the visual map and  701  indicates the LiDAR map. 
     The controller  250  of the robot  1  performs LiDAR SLAM where LiDAR sensing information  75  captured by the LiDAR sensor  220  is compared with LiDAR frames of the LiDAR map ( 701 ). As a result, as in S 76 , an image that is considered to have the same position by the controller is searched, and the controller determines that a current position of the robot is the frame node of v 5 . 
     In this case, the controller  250  of the robot  1  updates the visual map  70   v  by associating information  77  captured by the camera sensor  230  with the position of v 5 . 
     In case the LiDAR sensing information  75  captured by the LiDAR sensor  220  is the same as a LiDAR frame registered in the frame node of v 2  and the controller  250  determines that a current position of the robot is the frame node of v 2 , the controller  250  may add the captured image  77  to the frame node of v 2 . In this case, like  77   a  in  FIG.  12   , a new visual frame is added to the visual map ( 70   v ). 
     When a map that is previously drawn up needs to be updated with additional information in case the above-described embodiment is applied, the robot uses fusion SLAM. That is, the robot may perform localization using information acquired by any one sensor and may update a map with information acquired by another map. In this process, pieces of information of the maps preciously used for localization are maintained. Accordingly, a new map may only be added while an alignment between the maps is maintained. 
     While the robot  1  performs SLAM, each sensor may acquire information at an area with high accuracy of localization or at an area with low accuracy of localization, and the robot  1  may store the information. Additionally, the robot  1  may learn the stored information using an artificial intelligence module and may repeatedly apply the information acquired at the area with low accuracy of localization or at the area with high accuracy of localization to the pose graph. 
     To this end, the artificial intelligence unit  255  of the controller  250  is a type of learning processor. The artificial intelligence unit  255  may process position information cumulatively stored by the robot  1  and information acquired by sensors, and numerical values on accuracy of localization and may update the pose graph. 
     Artificial intelligence refers to a field of researching artificial intelligence or researching methodologies for creating artificial intelligence, and machine learning refers to a field of defining various problems in the field of artificial intelligence and researching methodologies for solving the problems. The machine learning is defined as an algorithm that improves the performance of a task through consistent experiences with the task. 
     An artificial neural network (ANN) is a model used in machine learning and may refer to any kind of model having a problem-solving capability, the model being composed of artificial neurons (nodes) forming a network by a combination of synapses. The ANN may be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value. 
     The ANN may include an input layer and an output layer. Optionally, the ANN may further include one or more hidden layers. Each layer may include one or more neurons, and the ANN may include synapses for connecting the neurons. In the ANN, each neuron may output function values of the activation function associated with input signals, weights, and deflections that are received through the synapses. 
     The model parameters refer to parameters determined through learning and include synapse connection weights, neuron deflections, and the like. Also, hyperparameters refer to parameters to be set before learning in a machine learning algorithm and includes a learning rate, the number of repetitions, a minimum placement size, an initialization function, and the like. 
     The training purpose of the ANN can be regarded as determining model parameters that minimize a loss function. The loss function may be used as an index for determining an optimal model parameter during the learning process of the ANN. 
     The machine learning may be classified as supervised learning, unsupervised learning, or reinforcement learning depending on the learning scheme. 
     The supervised learning may refer to a method of training the ANN while a label for learning data is given, and the label may refer to an answer (or a result value) to be inferred by the ANN when the learning data is input to the ANN. The unsupervised learning may refer to a method of training the ANN while the label for the learning data is not given. The reinforcement learning may refer to a learning method for training an agent defined in any embodiment to select an action or a sequence of actions that maximizes cumulative reward in each state. 
     Machine learning implemented using a deep neural network (DNN) including a plurality of hidden layers in the ANN will be called deep learning, and the deep learning is a portion of the machine learning. In the following description, the machine learning is used as a meaning including the deep learning. 
     For the robot  1 , the artificial intelligence unit  255  in  FIG.  2    may perform an artificial intelligence function. 
     In this case, the communication unit  280  of the robot  1  may transmit or receive data to or from external apparatuses such as the AI server  300 , which will be described in  FIG.  9   , or a robot for providing another artificial intelligence function through wired and wireless communication technologies. For example, the communication unit  280  may transmit or receive sensor information, user inputs, learning models, control signals, and the like to or from external apparatuses. 
     In this case, the communication technology used by the communication unit  280  includes Global System for Mobile Communication (GSM), code-division multiple access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN (WLAN), Wireless-Fidelity (Wi-Fi), Bluetooth™, Radio-Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, Near Field Communication (NFC), and the like. 
     The interface unit  290  may acquire various kinds of data. 
     In this case, the interface unit  290  may include a camera for receiving an image signal input, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, information acquired by the LiDAR sensor  220 , the camera sensor  230 , or the microphone refers to sensing data, sensor information, and the like. 
     The interface unit  290 , various kinds of sensors, the wheel encoder  260 , and the like may acquire input data or the like to be used when an output is acquired using a learning model and learning data for learning a model. The aforementioned elements may acquire raw input data. In this case, the controller  250  or the artificial intelligence unit  255  may extract an input feature as a preprocessing process for the input data. 
     The artificial intelligence unit  255  may train a model composed of an ANN using learning data. Here, the trained ANN may be called a learning model. The learning model may be used to infer a result value not for the learning data but for new input data, and the inferred value may be used as a determination basis for the robot  1  to perform a certain operation. 
     In this case, the artificial intelligence unit  255  may perform artificial intelligence processing along with the artificial intelligence unit  355  of the AI server  300 . 
     In this case, the artificial intelligence unit  255  may include a memory integrated or implemented in the robot  1 . Alternatively, the artificial intelligence unit  255  may be implemented using a separate memory, an external memory coupled to the robot  1 , or a memory held in an external apparatus. 
     The robot  1  may acquire at least one of internal information of the robot  1 , environmental information of the robot  1 , and user information using various sensors. 
     Sensors included in the robot  1  include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyroscope sensor, an inertial sensor, an RGB sensor, an infrared sensor (IR sensor), a finger scan sensor, an ultrasonic sensor, an optical sensor, a microphone, a LiDAR sensor  220 , a camera sensor  230 , a radar sensor and the like. 
     The above-described interface unit  290  may generate output in relation to vision, hearing or touch and the like. 
     In this case, the interface unit  290  may include a display unit outputting visual information, a speaker outputting auditory information, a haptic module outputting tactile information and the like. 
     A memory built in the robot  1  may store data supporting various functions of the robot  1 . For example, the memory may store input data, learning data, a learning model, a learning history, and the like which are acquired by the interface unit  290  or various kinds of sensors built in the robot  1 . 
     The controller  250  may determine at least one executable operation of the robot  1  on the basis of information determined or generated using a data analysis algorithm or a machine learning algorithm. Also, the controller  250  may control the elements of the robot  1  to perform the determined operation. 
     To this end, the controller  250  may request, retrieve, receive, or utilize data of the artificial intelligence unit  255  or the memory and may control the elements of the robot  1  to execute a predicted operation or an operation determined as being desirable among the at least one executable operation. 
     In this case, when there is a need for connection to an external apparatus in order to perform the determined operation, the controller  250  may generate a control signal for controlling the external apparatus and transmit the generated control signal to the external apparatus. 
     The controller  250  may acquire intention information with respect to a user input and may determine a user&#39;s requirements based on the acquired intention information. 
     In this case, the controller  250  may acquire intention information corresponding to user input using at least one or more of a speech-to-text (STT) engine for transforming voice input into character strings or a natural language processing (NLP) engine for acquiring intention information of natural language. 
     In this case, at least part of at least one or more of the STT engine or the NLP engine may include an artificial intelligence network trained based on a machine learning algorithm. Additionally, at least one or more of the STT engine or the NLP engine may be trained by the artificial intelligence unit  255 , or by the learning processor  340  of the AI server  300 , or by distributed processing thereof. 
     The controller  250  may collect history information including details of operation of the robot  1 , a user&#39;s feedback on operation of the robot and the like and may store the history information in the memory or the artificial intelligence unit  255 , or may transmit the history information to an external device such as the AI server  300  and the like. The collected history information may be used to update a learning model. 
     The controller  250  may control at least part of components of the robot  1  to drive an application program stored in the memory  170 . Further, the controller  250  may combine and operate two or more of the components included in the robot  1  to drive the application program. 
     Alternately, an additional artificial intelligence (AI) server communicating with the robot  1  may be provided and may process information supplied by the robot  1 . 
       FIG.  13    shows a configuration of an AI server  300  according to an embodiment. 
     An artificial intelligence server, i.e., an AI server  300 , may denote a device that trains an artificial neural network using a machine learning algorithm or that uses a trained artificial neural network. The AI server  300 , which includes a plurality of servers, may perform distributed processing and may be defined as a 5G network. In this case, the AI server  300  may be included as a partial configuration of an AI device  100  and may perform at least part of AI processing together with the AI device  100 . 
     The AI server  300  may include a communication unit  310 , a memory  330 , a learning processor  340  and a processor  360  and the like. 
     The communication unit  310  may transmit or receive data to or from an external device such as the robot  1  and the like. 
     The memory  330  may include a model storage unit  331 . The model storage unit  331  may store a model  231   a  (or an artificial neural network) that is being trained or is trained through a learning processor  340 . 
     The learning processor  340  may train the artificial neural network  331   a  using learning data. A learning model may be used in the state of being mounted onto the AI server  300  of the artificial neural network, or may be used in the state of being mounted onto an external device such as the robot  1  and the like. 
     The learning model may be implemented as hardware, software or a combination thereof. When all or part of the learning model is implemented as software, one or more instructions constituting the learning model may be stored in the memory  330 . 
     The processor  360  may infer result values on new input data using the learning model, and may generate responses or control instructions based on the inferred result values. 
       FIG.  14    shows a process of updating a pose graph on the basis of artificial intelligence during SLAM of a robot according to an embodiment. 
     While performing SLAM, the robot acquires information on an area with high accuracy of localization or an area with low accuracy of localization. That is, the robot supplies information sensed by the LiDAR sensor  220  or the camera sensor  230 , accuracy of localization preformed by the robot on the basis of the information, and position information to the artificial intelligence unit  255  or the AI server  300  (S 91 ). 
     The artificial intelligence unit  255  or the artificial intelligence server  300  compares accuracy of pieces of information acquired by each sensor according to position information, using the supplied information. The artificial intelligence unit  255  or the artificial intelligence server  300  compares sensor information acquired at an area with high accuracy or with low accuracy with information previously stored in the map storage, and determines whether to update a map (S 92 ). 
     Then the controller  250  updates the map using sensed information according to the determination to update the map (S 93 ). As an example, the controller  250  may update a LiDAR frame/visual frame registered in the pose graph. 
     According to the process of  FIG.  14   , while the robot  1  performs SLAM, each sensor may acquire information at an area with high accuracy of localization or at an area with low accuracy of localization, and the robot  1  may store the information. Additionally, the robot  1  may learn the stored information using an artificial intelligence module and may repeatedly apply the information acquired at the area with low accuracy of localization or at the area with high accuracy of localization to the pose graph. 
     AI technologies may be applied to the robot  1 , and the robot  1  may be implemented as a guide robot, a transportation robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned aerial robot, and the like. 
     The robot  1  may include a robot control module for controlling operations, and the robot control module may denote a software module or a chip in which a software module is implemented as hard ware. 
     The robot  1  may acquire its state information, may detect (recognize) a surrounding environment and a surrounding object, may generate map data, may determine a moving path and a driving plan, may determine a response to user interaction or may determine an operation, using sensor information acquired from various types of sensors. 
     The robot  1  may use sensor information acquired by at least one or more sensors among a LiDAR sensor, a radar sensor, and a camera sensor to determine a moving path and a driving plan. 
     The robot  1  may perform the above-described operations using a learning model comprised of at least one or more artificial neural networks. For example, the robot  1  may recognize a surrounding environment and a surrounding object using the learning model, and may determine an operation using information on the recognized surrounding environment or object. The learning model may be directly learned by the robot  1  or by an external device such as an AI server  300  and the like. 
     In this case, the robot  1  may perform operations by directly using the learning model and generating results. The robot  1  may also perform operations by transmitting sensor information to an external device such as an AI server  300  and the like and by receiving results that are generated as a result. 
     The robot  1  may determine a moving path and a driving plan using at least one or more of map data, object information detected from sensor information, or object information acquired from an external device, and may drive on the basis of the determined moving path and driving plan by controlling a driving unit. 
     Map data may include object identification information on various objects in a space in which the robot  1  moves. For example, the map data may include object identification information on fixed objects such as a wall, a door and the like, and on movable objects such as a flower pot, a desk and the like. Additionally, the object identification information may include a name, a sort, a distance, a location and the like. 
     Further, the robot  1  may perform operations or may perform driving by controlling the driving unit on the basis of control/interactions of a user. In this case, the robot  1  may acquire intention information on interactions according to operations of the user or utterance of voices of the user, may determine responses on the basis of the acquired intention information, and may perform operations. 
     The controller  250  according to embodiments of the present disclosure may be equipped with an artificial intelligence module. In this case, the controller  250  may be equipped with an artificial intelligence module to search for a LiDAR frame similar to information acquired at a current position, among LiDAR frames stored in the map storage  210 . For example, a deep learning network may be used to search an image, and the controller  250  including the deep learning network may increase the speed of a search for an image. 
     Also, using the wheel odometry information of driving unit, the controller  250  increases search speed by searching visual frame or LiDAR frame in map storage  210 . 
     Although in embodiments, all the elements that constitute the embodiments of the present disclosure are described as being coupled to one or as being coupled to one so as to operate, the disclosure is not limited to the embodiments. One or more of all the elements may be optionally coupled to operate within the scope of the present disclosure. Additionally, each of the elements may be implemented as single independent hardware, or some or all of the elements may be optionally combined and implemented as a computer program that includes a program module for performing some or all of the combined functions in single hardware or a plurality of hardware. Codes or segments that constitute the computer program may be readily inferred by one having ordinary skill in the art. The computer program is recorded on computer-readable media and read and executed by a computer to implement the embodiments. Storage media that store computer programs includes storage media magnetic recording media, optical recording media, and semiconductor recording devices. Additionally, the computer program that embodies the embodiments includes a program module that is transmitted in real time through an external device. 
     The embodiments of the present disclosure have been described. However, the embodiments may be changed and modified in different forms by one having ordinary skill in the art. Thus, it should be understood that the changes and modifications are also included within the scope of the present disclosure.