Map Generator

Abstract

To reduce the data amount of map information while suppressing the decrease in accuracy of the map information.SOLUTION: A map generation apparatus 50 includes: an in-vehicle detection unit configured to detect a situation around a subject vehicle; a feature point extraction unit 141 configured to extract feature points from detection data acquired from the in-vehicle detection unit; a map generation unit 17 configured to generate a map with the feature points extracted from the detection data; a recognition unit 142 configured to recognize a landmark on the map generated by the map generation unit 17; an importance determination unit 143 configured to determine an importance of the landmark recognized by the recognition unit 142; and a correction unit 144 configured to correct the number of the feature points included in the map generated by the map generation unit 17, based on the importance determined by the importance determination unit 143.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a map generating device that generates a map used to acquire the position of a vehicle. [Background technology]

[0002] Conventionally, one such device known in the art is one that extracts feature points from an image captured by an onboard camera while the vehicle is traveling, and stores a feature point image including the extracted feature points as reference data used to acquire the position of the vehicle (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2013-32953 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, depending on the number of objects and backgrounds included in the angle of view of the vehicle-mounted camera, the number of feature points included in the feature point image increases, and the data size of the feature point image also increases accordingly. Therefore, simply storing the feature point image as in the device described in Patent Document 1 may consume a large amount of storage capacity. [Means for solving the problem]

[0005] A map generating device according to one aspect of the present invention is a map generating device that generates a map used to acquire the position of a vehicle. The map generating device includes an on-board detector that detects the surroundings of the vehicle, a feature point extraction unit that extracts feature points from detection data acquired by the on-board detector, a map generating unit that generates a map using the feature points extracted from the detection data, a recognition unit that recognizes landmarks on the map generated by the map generating unit, an importance determination unit that determines the importance of the landmarks recognized by the recognition unit, and a correction unit that corrects the number of feature points included in the map generated by the map generating unit based on the importance determined by the importance determination unit. The correction unit increases the intervals between key frames included in the map generated by the map generation unit so that the number of feature points decreases as the importance determined by the importance determination unit decreases. Effect of the Invention

[0006] According to the present invention, it is possible to reduce the amount of data in the map information while suppressing a decrease in the accuracy of the map information. [Brief description of the drawings]

[0007] [Figure 1] 1 is a block diagram showing an overall configuration of a vehicle control system according to an embodiment of the present invention; [Diagram 2] 1 is a block diagram showing a configuration of a main part of a map generating device according to an embodiment of the present invention. [Figure 3A] FIG. 4 is a diagram showing an example of a captured image acquired by a camera. [Figure 3B] FIG. 11 is a diagram showing another example of a captured image acquired by the camera. [Figure 4] 3 is a flowchart showing an example of processing executed by a CPU of the controller of FIG. 2; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to Figs. 1 to 4. A map generating device according to an embodiment of the present invention can be applied to a vehicle having an automatic driving function, that is, an automatic driving vehicle. The vehicle to which the map generating device according to the present embodiment is applied may be called the host vehicle to distinguish it from other vehicles. The host vehicle may be any of an engine vehicle having an internal combustion engine (engine) as a driving source, an electric vehicle having a driving motor as a driving source, and a hybrid vehicle having an engine and a driving motor as driving sources. The host vehicle can run not only in an automatic driving mode in which no driving operation by the driver is required, but also in a manual driving mode in which the driver operates the vehicle.

[0009] First, a schematic configuration relating to automatic driving will be described. Fig. 1 is a block diagram showing a schematic overall configuration of a vehicle control system 100 having a map generating device according to an embodiment of the present invention. As shown in Fig. 1, the vehicle control system 100 mainly includes a controller 10, an external sensor group 1 communicably connected to the controller 10, an internal sensor group 2, an input / output device 3, a positioning unit 4, a map database 5, a navigation device 6, a communication unit 7, and an actuator AC for driving.

[0010] The external sensor group 1 is a collective term for a plurality of sensors (external sensors) that detect the external situation, which is information about the surroundings of the vehicle. For example, the external sensor group 1 includes a lidar that measures the distance from the vehicle to obstacles in the vicinity by measuring the scattered light in response to the omnidirectional irradiation light of the vehicle, a radar that detects other vehicles and obstacles in the vicinity of the vehicle by irradiating electromagnetic waves and detecting reflected waves, and a camera that is mounted on the vehicle and has an imaging element such as a CCD or CMOS to capture images of the surroundings (front, rear, and sides) of the vehicle.

[0011] The internal sensor group 2 is a collective term for a plurality of sensors (internal sensors) that detect the driving state of the vehicle. For example, the internal sensor group 2 includes a vehicle speed sensor that detects the vehicle speed of the vehicle, an acceleration sensor that detects the acceleration in the forward and backward directions and the acceleration in the left and right directions (lateral acceleration) of the vehicle, a rotation speed sensor that detects the rotation speed of the driving source, a yaw rate sensor that detects the rotation angular velocity around the vertical axis of the center of gravity of the vehicle, etc. The internal sensor group 2 also includes sensors that detect the driving operations of the driver in the manual driving mode, such as the operation of the accelerator pedal, the operation of the brake pedal, the operation of the steering wheel, etc.

[0012] The input / output device 3 is a general term for devices that receive commands from the driver and output information to the driver. For example, the input / output device 3 includes various switches through which the driver inputs various commands by operating operating members, a microphone through which the driver inputs commands by voice, a display that provides information to the driver through displayed images, and a speaker that provides information to the driver by voice.

[0013] The positioning unit (GNSS unit) 4 has a positioning sensor that receives positioning signals transmitted from positioning satellites. The positioning satellites are artificial satellites such as GPS satellites and quasi-zenith satellites. The positioning unit 4 measures the current position (latitude, longitude, altitude) of the vehicle using the positioning information received by the positioning sensor.

[0014] The map database 5 is a device that stores general map information used in the navigation device 6, and is configured with, for example, a hard disk or a semiconductor element. The map information includes road position information, road shape (curvature, etc.) information, intersection and branch point position information, and speed limit information set for the road. Note that the map information stored in the map database 5 is different from the highly accurate map information stored in the storage unit 12 of the controller 10.

[0015] The navigation device 6 is a device that searches for a target route on roads to a destination input by the driver and provides guidance along the target route. The input of the destination and guidance along the target route are performed via the input / output device 3. The target route is calculated based on the current position of the vehicle measured by the positioning unit 4 and map information stored in the map database 5. The current position of the vehicle can also be measured using detection values ​​of the external sensor group 1, and the target route may be calculated based on this current position and highly accurate map information stored in the memory unit 12.

[0016] The communication unit 7 is connected to the Internet and a mobile phone network. etc. The vehicle communicates with various servers (not shown) via a network including a wireless communication network such as the Internet, and acquires map information, driving history information, traffic information, and the like from the server periodically or at any timing. The network includes not only a public wireless communication network but also a closed communication network provided for each predetermined management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like. The acquired map information is output to the map database 5 or the storage unit 12, where the map information is updated.

[0017] The actuators AC are driving actuators for controlling the driving of the host vehicle. When the driving source for driving is an engine, the actuators AC include a throttle actuator that adjusts the opening of a throttle valve (throttle opening) of the engine. When the driving source for driving is a driving motor, the actuators AC include the driving motor. The actuators AC also include a brake actuator that operates the braking device of the host vehicle and a steering actuator that drives the steering device.

[0018] The controller 10 is composed of an electronic control unit (ECU). More specifically, the controller 10 includes a computer having an arithmetic unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM, a RAM, and other peripheral circuits (not shown) such as an I / O interface. Note that although a plurality of ECUs with different functions, such as an engine control ECU, a travel motor control ECU, and a braking device ECU, can be provided separately, the controller 10 is shown in FIG. 1 as a collection of these ECUs for the sake of convenience.

[0019] The memory unit 12 stores highly accurate, detailed map information (called high-precision map information). The high-precision map information includes road position information, road shape (curvature, etc.) information, road gradient information, intersection and branch point position information, number of lanes information, lane width and position information for each lane (lane center position and lane position boundary information), position information of landmarks (traffic lights, signs, buildings, etc.) as markers on the map, and road surface profile information such as road surface irregularities. The high-precision map information stored in the memory unit 12 includes road position information, road shape information (curvature, etc.) information, road gradient information, intersection and branch point position information, number of lanes information, lane width information, lane position information (lane center position and lane position boundary information), lane position information, road surface profile information such as road surface irregularities, etc. Self The map information includes map information acquired from outside the vehicle, for example, map information acquired via a cloud server (called a cloud map), and map information created by the vehicle itself using detection values ​​from the external sensor group 1, for example, map information made of point cloud data generated by mapping using a technology such as SLAM (Simultaneous Localization and Mapping) (called an environmental map).The storage unit 12 also stores information on various control programs, threshold values ​​used in the programs, and other information.

[0020] The calculation unit 11 has, as its functional configuration, a vehicle position recognition unit 13, an external environment recognition unit 14, an action plan generation unit 15, a driving control unit 16, and a map generation unit 17.

[0021] The vehicle position recognition unit 13 recognizes the position of the vehicle on the map (the vehicle position) based on the vehicle position information obtained by the positioning unit 4 and the map information in the map database 5. The vehicle position may be recognized using the map information stored in the storage unit 12 and the surrounding information of the vehicle detected by the external sensor group 1, thereby making it possible to recognize the vehicle position with high accuracy. When the vehicle position can be measured by a sensor installed externally on or beside the road, the vehicle position can also be recognized by communicating with the sensor via the communication unit 7.

[0022] The external environment recognition unit 14 recognizes the external situation around the vehicle based on signals from the external sensor group 1, such as a lidar, radar, and camera. For example, it recognizes the positions, running speeds, and accelerations of surrounding vehicles (vehicles ahead and vehicles behind) traveling around the vehicle, the positions of surrounding vehicles stopped or parked around the vehicle, and the positions and states of other objects. The other objects include signs, traffic lights, markings (road markings) such as road division lines and stop lines, buildings, guardrails, utility poles, signs, pedestrians, bicycles, and the like. The states of the other objects include the colors of traffic lights (red, green, yellow), and the moving speeds and directions of pedestrians and bicycles. Some of the stationary objects among the other objects constitute landmarks that serve as indicators of positions on a map, and the external environment recognition unit 14 also recognizes the positions and types of the landmarks.

[0023] The action plan generating unit 15 generates a running trajectory (target trajectory) of the vehicle from the current time to a predetermined time ahead based on, for example, a target route calculated by the navigation device 6, the vehicle position recognized by the vehicle position recognition unit 13, and the external situation recognized by the external environment recognition unit 14. When there are multiple trajectories that are candidates for the target trajectory on the target route, the action plan generating unit 15 selects an optimal trajectory that satisfies criteria such as complying with laws and regulations and traveling efficiently and safely, and sets the selected trajectory as the target trajectory. Then, the action plan generating unit 15 generates an action plan according to the generated target trajectory. The action plan generating unit 15 generates various action plans corresponding to the running modes such as overtaking running to overtake a preceding vehicle, lane changing running to change the running lane, following running to follow a preceding vehicle, lane keeping running to maintain the lane so as not to deviate from the running lane, decelerating running or accelerating running. When generating the target trajectory, the action plan generating unit 15 first determines the running mode, and generates the target trajectory based on the running mode.

[0024] In the autonomous driving mode, the driving control unit 16 controls each actuator AC so that the vehicle travels along the target trajectory generated by the behavior plan generating unit 15. More specifically, the driving control unit 16 calculates a required driving force for obtaining the target acceleration per unit time calculated by the behavior plan generating unit 15, taking into consideration the driving resistance determined by the road gradient in the autonomous driving mode. Then, for example, the driving control unit 16 feedback-controls the actuators AC so that the actual acceleration detected by the internal sensor group 2 becomes the target acceleration. That is, the driving control unit 16 controls the actuators AC so that the vehicle travels at the target vehicle speed and the target acceleration. In the manual driving mode, the driving control unit 16 controls each actuator AC in response to a driving command (such as a steering operation) from the driver acquired by the internal sensor group 2.

[0025] The map generating unit 17 generates an environmental map consisting of three-dimensional point cloud data using detection values ​​detected by the external sensor group 1 while driving in manual driving mode. Specifically, from the captured image acquired by the camera 1a, edges showing the contours of objects are extracted based on the brightness and color information of each pixel, and feature points are extracted using the edge information. The feature points are, for example, intersections of edges, and correspond to corners of buildings and corners of road signs. The map generating unit 17 sequentially plots the extracted feature points on the environmental map, thereby generating an environmental map of the area around the road on which the vehicle has traveled. Instead of the camera, data acquired by a radar or a lidar may be used to extract feature points of objects around the vehicle, and an environmental map may be generated. In addition, when generating the environmental map, the map generating unit 17 determines whether or not landmarks such as traffic lights, signs, and buildings as marks on the map are included in the captured image acquired by the camera, for example, by pattern matching processing. Then, when it is determined that a landmark is included, the position and type of the landmark on the environmental map are recognized based on the captured image. This landmark information is included in the environmental map and stored in the memory unit 12.

[0026] The vehicle position recognition unit 13 performs a process of estimating the position of the vehicle in parallel with the map creation process by the map generation unit 17. That is, the vehicle position is acquired based on the change in the position of the feature points over time. The vehicle position recognition unit 13 also estimates the vehicle position based on the relative positional relationship between the vehicle and the landmarks around the vehicle. The map creation process and the position estimation process are performed simultaneously according to, for example, a SLAM algorithm. The map generation unit 17 can generate an environmental map not only when driving in a manual driving mode, but also when driving in an automatic driving mode. If an environmental map has already been generated and stored in the storage unit 12, the map generation unit 17 may update the environmental map with the newly obtained feature points.

[0027] Incidentally, when recognizing (estimating) the vehicle position, the vehicle position recognition unit 13 first extracts feature points from a captured image in which a landmark is included within the angle of view acquired by the camera 1a. Then, the extracted feature points are matched with an environmental map (point cloud data) to recognize landmarks (feature points corresponding to the landmarks) on the environmental map. Next, the vehicle position recognition unit 13 calculates a distance between the vehicle and the landmark based on the position of the landmark on the captured image, and estimates the position of the vehicle on the environmental map based on the calculated distance and the position of the landmark on the environmental map. At this time, the greater the number of feature points constituting the environmental map, the higher the accuracy of matching, and the more accurately the vehicle position can be estimated. On the other hand, as the number of feature points constituting the environmental map increases, the amount of data on the environmental map increases, and the capacity of the storage device is significantly consumed. Therefore, in this embodiment, the map generating device is configured as follows to reduce the amount of data on the environmental map while suppressing a decrease in the estimation accuracy of the vehicle position.

[0028] Fig. 2 is a block diagram showing a configuration of a main part of a map generating device 50 according to an embodiment of the present invention. This map generating device 50 generates a point cloud map (environmental map) based on feature points extracted from an image captured by a camera 1a, and constitutes a part of the vehicle control system 100 in Fig. 1. As shown in Fig. 2, the map generating device 50 has a controller 10, a camera 1a, a radar 1b, and a lidar 1c.

[0029] The camera 1a is a monocular camera having an imaging element (image sensor) such as a CCD or a CMOS, and constitutes a part of the external sensor group 1 in FIG. 1. The camera 1a may be a stereo camera. The camera 1a captures images of the surroundings of the vehicle. The camera 1a is attached, for example, at a predetermined position in the front of the vehicle, and continuously captures images of the space in front of the vehicle to obtain image data of the object (hereinafter, referred to as captured image data or simply captured image). The camera 1a outputs the captured image to the controller 10. The radar 1b is mounted on the vehicle and detects other vehicles and obstacles around the vehicle by irradiating electromagnetic waves and detecting reflected waves. The radar 1b outputs a detection value (detection data) to the controller 10. The lidar 1c is mounted on the vehicle and measures scattered light in response to irradiated light in all directions of the vehicle to detect the distance from the vehicle to obstacles in the vicinity. The lidar 1c outputs a detection value (detection data) to the controller 10.

[0030] The controller 10 has, as functional components carried out by the calculation unit 11 (FIG. 1), a feature point extraction unit 141, a recognition unit 142, an importance determination unit 143, a correction unit 144, and a map generation unit 17. The feature point extraction unit 141, the recognition unit 142, the importance determination unit 143, and the correction unit 144 are configured by, for example, the external environment recognition unit 14 in FIG. 1.

[0031] The feature point extraction unit 141 extracts feature points from the captured image acquired by the camera 1a. More specifically, the feature point extraction unit 141 extracts feature points from each captured image (hereinafter, sometimes referred to as a frame image or simply a frame) acquired by the camera 1a on a frame-by-frame basis. FIGS. 3A and 3B are diagrams showing an example of a captured image acquired by the camera 1a. The recognition unit 142 recognizes landmarks on a map included in the captured image acquired by the camera 1a. For example, when the captured image acquired by the camera 1a is the image shown in FIG. 3A, an intersection IS, a traffic light SG, a stop line SL, a pedestrian crossing CW, etc. are recognized as landmarks.

[0032] The importance determination unit 143 determines the importance of the landmark recognized by the recognition unit 142. When traveling through a point where an intersection IS, a traffic light SG, a stop line SL, a pedestrian crossing CW, etc. are present as shown in FIG. 3A, a higher position estimation accuracy is required from the viewpoint of safety. In other words, it is required that the distance between the landmarks and the vehicle is calculated more accurately. Therefore, the importance determination unit 143 sets the importance of those landmarks to the highest level, "high". On the other hand, the importance of objects that are not treated as landmarks, such as trees TR and roadside shrubbery as shown in FIG. 3B, is set to the lowest level, "low". In addition, the importance of landmarks that are not included in intersections, traffic lights, etc., such as road signs and buildings, is set to a level, "medium", between "high" and "low". For the sake of simplicity of explanation, an example in which the importance levels are set to three levels, "high", "medium", and "low", is used, but the importance levels may be other than three levels.

[0033] The importance determination unit 143 recognizes the speed limit set for the road on which the vehicle is currently traveling, based on map information stored in the map database 5. The importance determination unit 143 sets a range (section on the road) to which the determined importance is applied, based on the recognized speed limit. At this time, the importance determination unit 143 sets a section from the current position of the vehicle to a point in the traveling direction by a distance determined according to the speed limit, as the range to which the importance is applied. In general, the faster the traveling speed of the vehicle, the faster the moving speed of the feature points on the captured image becomes, and it becomes more difficult to match the feature points on the captured image with the feature points on the environmental map. Therefore, the importance determination unit 143 sets a wider range (section) to which the importance is applied, the higher the speed limit, so that the feature points can be matched with high accuracy. The importance determination unit 143 includes information indicating the determined importance and the application range of the importance (hereinafter referred to as importance information) in the environmental map and stores it in the storage unit 12.

[0034] The modification unit 144 modifies the number of feature points by thinning out the feature points on the environmental map based on the importance information stored in the storage unit 12. Specifically, the modification unit 144 modifies the number of feature points so that the lower the importance, the fewer the number of feature points in the section to which the importance is applied. More specifically, the modification unit 144 reduces the number of feature points (feature points extracted from each frame by the feature point extraction unit 141) of each frame acquired by the camera 1a in the section to which the importance is applied. In addition, the modification unit 144 sets the number of frames between key frames, that is, the key frame interval D, based on the importance information stored in the storage unit 12. The key frame is a frame selected based on the key frame interval D from each frame acquired by the camera 1a, and among the feature points extracted from each frame by the feature point extraction unit 141, the feature points extracted from the key frame are stored in the environmental map. Therefore, the smaller the key frame interval, the greater the number of feature points. Specifically, the modifying unit 144 sets the key frame interval D such that the higher the importance, the smaller the key frame interval D in the section to which that importance is applied.

[0035] Fig. 4 is a flowchart showing an example of processing executed by the controller 10 of Fig. 2 according to a predetermined program, particularly an example of processing related to map generation. In the following, an example will be described in which the vehicle travels along a route RT. The route RT is, for example, a route from the user's home to a predetermined point (for example, a workplace or a hospital). The processing shown in the flowchart of Fig. 4 is repeated at a predetermined interval while the vehicle travels along the route RT in manual driving mode.

[0036] First, in step S11, it is determined whether the vehicle 101 has traveled the route RT in the past, i.e., whether this is the first travel. If the result in step S11 is affirmative, in step S12, a captured image is acquired by the camera 1a. Next, in step S13, feature points are extracted from the captured image acquired in step S12, and an environmental map is generated based on the extracted feature points. Specifically, the extracted feature points are plotted on the environmental map. At this time, the feature points are extracted at a key frame interval D corresponding to the initial value of the importance "low". In step S14, it is determined, for example, by pattern matching processing, whether the captured image acquired in step S12 includes landmarks such as traffic lights, signs, and buildings as indicators of positions on the map.

[0037] If the result in step S14 is negative, the process proceeds to step S16. If the result in step S14 is positive, in step S15, the position and type of landmark (intersection, traffic light, etc.) on the environmental map are recognized based on the captured image acquired in step S12. At this time, information indicating the position and type of landmark (landmark information) is generated and stored in the memory unit 12 as part of the environmental map or as associated data of the environmental map. In step S16, the importance of the landmark is determined based on the type of the landmark recognized in step S15. Note that when the captured image acquired in step S12 is an image as shown in FIG. 3B, that is, an image that does not include a landmark, the landmark is not recognized in step S15, and the importance is determined to be "low."

[0038] In step S17, the application range of the importance determined in step S16 is set based on the speed limit set for the road on which the vehicle is currently traveling. In step S18, information (importance information) indicating the importance determined in step S16 and the application range set in step S17 is generated and stored in the storage unit 12 as part of the environmental map or as associated data of the environmental map. Note that when step S14 is executed, if importance information whose application range includes the current traveling position of the vehicle has already been stored in the storage unit 12, the processes of steps S14 to S18 (generation process of importance information) may be skipped.

[0039] In step S19, it is determined whether the vehicle has reached a predetermined point, that is, the end point of the route RT. If the result in step S19 is negative, the process is terminated. If the result in step S19 is positive, in step S20, based on the importance information stored in the storage unit 12, the feature points on the environmental map generated during traveling along the route RT are thinned out to correct the number of feature points constituting the environmental map. For example, feature points are deleted so that the number of feature points corresponding to each frame acquired by the camera 1a in the section to which the importance "low" is applied is reduced to 1 / 3. Feature points are deleted so that the number of feature points corresponding to each frame acquired by the camera 1a in the section to which the importance "medium" is applied is reduced to 1 / 2. On the other hand, the number of feature points in the section to which the importance "high" is applied is not changed. Also, for example, key frames are deleted so that the key frame interval D in the section to which the importance "low" is applied is tripled. Key frames are deleted so that the key frame interval D in the section to which the importance "medium" is applied is doubled. On the other hand, the key frame interval D in the section to which the importance "high" is applied is not changed.

[0040] In the flowchart of FIG. 4, the process of steps S12 to S18 is executed during the first travel of the route RT, but the process of steps S12 to S18 may also be executed during the second or subsequent travel of the route RT. This allows the environmental map and importance information of the route RT to be updated to the latest state. In addition, in the flowchart of FIG. 4, step S20 is executed when the host vehicle reaches the end point of the route RT, but step S20 may be executed at a predetermined timing while traveling the route RT. For example, step S20 may be executed every time the host vehicle travels a predetermined distance while traveling the route RT. Also, for example, step S20 may be executed every time the host vehicle travels a predetermined time while traveling the route RT.

[0041] Also, in the flowchart of Fig. 4, landmark information is generated in steps S14 to S15, and importance information is stored in steps S16 to S18. However, if landmark information corresponding to the route RT has been acquired from outside the vehicle and has already been stored in the storage unit 12, the processing of steps S14 to S18 may be skipped. In that case, when the result of step S19 is YES, that is, when the environmental map corresponding to the route RT is generated, the processing of steps S16 to S18 may be executed based on the landmark information corresponding to the route RT, and then the process may proceed to step S20. By using existing landmark information in this way, the processing can be simplified.

[0042] According to the embodiment of the present invention, the following advantageous effects can be obtained. (1) The map generating device 50 that generates a map used to acquire the position of the vehicle uses a camera that detects the surroundings of the vehicle. 1a And the camera 1aThe map generating unit 17 generates a map using the feature points extracted from the captured image, a recognition unit 142 recognizes landmarks on the map generated by the map generating unit 17, an importance determination unit 143 determines the importance of the landmark recognized by the recognition unit 142, and a correction unit 144 corrects the number of feature points included in the map generated by the map generating unit 17 based on the importance determined by the importance determination unit 143. This makes it possible to reduce the amount of data in the map information while suppressing a decrease in the accuracy of the map information.

[0043] (2) The modification unit 144 reduces the number of feature points included in the map generated by the map generation unit 17 based on the importance determined by the importance determination unit 143. In addition, the modification unit 144 adjusts the intervals between key frames included in the map generated by the map generation unit 17 so that the number of feature points decreases as the importance determined by the importance determination unit 143 decreases. Large Furthermore, the importance determining unit 143 determines the importance of the landmark according to the type of the landmark recognized by the recognizing unit 142. This makes it possible to further suppress a decrease in the accuracy of the map information.

[0044] (3) The correction unit 144 sets a section on a road to which importance is applied based on the speed limit set for the road on which the vehicle is traveling, and corrects, based on the importance, the number of feature points corresponding to the set section among the feature points included in the map generated by the map generation unit 17. This makes it possible to accurately match the feature points on the captured image with the feature points on the environmental map, even in a section where the traveling speed of the vehicle increases and the moving speed of the feature points on the captured image of the camera 1a is expected to increase, and to appropriately correct the number of feature points.

[0045] The above embodiment can be modified in various ways. Some modified examples will be described below. In the above embodiment, the camera 1a detects the surroundings of the vehicle, but the on-board detector may have any configuration as long as it detects the surroundings of the vehicle. For example, the on-board detector may be a radar 1b or a lidar 1c. In the above embodiment, the process shown in FIG. 4 is executed while driving in the manual driving mode, but the process shown in FIG. 4 may be executed while driving in the automatic driving mode.

[0046] In the above embodiment, the importance is determined in step S16 according to the type of the landmark recognized in step S15, but the method of determining the importance is not limited to this. For example, when a plurality of landmarks are recognized in step S15, the type of each landmark may be recognized and the importance of each landmark may be determined. Then, the statistics of the importance of each landmark may be included in the importance information. For example, the statistics of the importance of each landmark (average value or median value) may be calculated by setting the importance of "high" to 3, the importance of "medium" to 2, and the importance of "low" to 1, and the calculation result may be included in the importance information. Also, the number of feature points may be corrected in step S20 based on the statistics of the importance of each landmark calculated in this way. Furthermore, in the above embodiment, the map generating device 50 is applied to an autonomous vehicle, but the map generating device 50 can be applied to vehicles other than autonomous vehicles. For example, the map generating device 50 can be applied to a manually driven vehicle equipped with ADAS (Advanced driver-assistance systems).

[0047] The above description is merely an example, and the present invention is not limited to the above-mentioned embodiment and modifications, as long as the features of the present invention are not impaired. One or more of the above-mentioned embodiment and modifications can be arbitrarily combined, and modifications can also be combined with each other. [Explanation of symbols]

[0048] 1a camera, 1b radar, 1c lidar, 10 controller, 50 map generating device, 141 feature point extraction unit, 142 recognition unit, 143 importance determination unit, 144 correction unit, 17 map generating unit, 100 vehicle control system

Claims

1. A map generating device that generates a map used to acquire the position of a vehicle, An on-board detector that detects the surroundings of the vehicle; a feature point extraction unit that extracts feature points from the detection data acquired by the vehicle-mounted detector; a map generating unit that generates a map using feature points extracted from the detection data; a recognition unit that recognizes landmarks on the map generated by the map generation unit; an importance determination unit that determines the importance of the landmark recognized by the recognition unit; a correction unit that corrects the number of feature points included in a map generated by the map generation unit based on the importance determined by the importance determination unit, the modification unit increases the spacing between key frames included in the map generated by the map generation unit so that the number of feature points decreases as the importance determined by the importance determination unit decreases.

2. A map generating device that generates a map used to acquire the position of a vehicle, An on-board detector that detects the surroundings of the vehicle; a feature point extraction unit that extracts feature points from the detection data acquired by the vehicle-mounted detector; a map generating unit that generates a map using feature points extracted from the detection data; a recognition unit that recognizes landmarks on the map generated by the map generation unit; an importance determination unit that determines the importance of the landmark recognized by the recognition unit; a correction unit that corrects the number of feature points included in a map generated by the map generation unit based on the importance determined by the importance determination unit, the modification unit sets a section on the road to which the importance is to be applied based on a speed limit set for the road on which the vehicle is traveling, and modifies, based on the importance, the number of feature points corresponding to the set section among the feature points included in the map generated by the map generation unit.

3. In the map generating device according to claim 1, the modification unit sets a section on the road to which the importance is to be applied based on a speed limit set for the road on which the vehicle is traveling, and modifies, based on the importance, the number of feature points corresponding to the set section among the feature points included in the map generated by the map generation unit.

4. A map generating device according to any one of claims 1 to 3, the modification unit reduces the number of feature points included in the map generated by the map generation unit so that the number of feature points decreases as the importance determined by the importance determination unit decreases.

5. A map generating device according to any one of claims 1 to 4, The map generating device, wherein the importance determining unit determines the importance of the landmark according to the type of the landmark recognized by the recognition unit.

Images