High Definition 3D Mapping

Abstract

The present disclosure provides "high definition 3D mapping". A system includes a processor and a memory. The memory stores instructions executable by the processor to: determine a plurality of waypoints for mapping an area by analyzing received aerial images of the area; select an optimal number of waypoints for mapping; and actuate a vehicle to traverse a route determined to cover the selected waypoints.

Description

Technical Field

[0001] The present invention relates to the field of vehicle navigation and, more particularly, to vehicle maps. Background Art

[0002] Vehicles such as autonomous vehicles or semi-autonomous vehicles can use data from LIDAR (light detection and ranging) sensors to help navigate. LIDAR sensors can be used to generate point cloud data, that is, a collection of data describing points in three dimensions. In addition, autonomous vehicles can compare its substantially real-time LIDAR point cloud data with a three-dimensional (3D) map of the area where the vehicle operates in order to navigate within the area. The 3D map of the area can be generated from LIDAR data collected by one or more mapping vehicles, which have previously crossed the roads in the area to obtain the point cloud data of the map. However, the problem with using current technology is that the mapping vehicle can cross the road section in the area to be mapped, etc., but the collected data is not enough to create a 3D map. When this happens, additional data collection is required, for example, usually, it is necessary to cross the road section or other parts of the area being mapped once or more. Summary of the invention

[0003] Introduction

[0004] A system is disclosed herein, the system comprising a processor and a memory. The memory stores instructions executable by the processor to: determine a plurality of waypoints for mapping an area by analyzing received aerial images of the area; select an optimal number of waypoints for mapping; and actuate a vehicle to traverse a route determined to cover the selected waypoints.

[0005] Each of the selected waypoints may be at least on a road surface and an intersection.

[0006] The selected waypoints may include a first point and a second point on a road segment, and a curve fitted to the first point and the second point may correspond to the road segment between the first point and the second point.

[0007] The instructions may also include instructions for at least one of generating and updating a map based at least in part on the received LIDAR sensor data, the map covering an area included in the received aerial image.

[0008] The map may include one or more roads and location coordinates of road features including traffic signs and buildings.

[0009] The instructions may also include instructions for routing the vehicle to traverse each of the selected waypoints at least some predetermined number of times.

[0010] The instructions may also include instructions for outputting an image to a display device, wherein the image includes the region having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on a number of traversal times of the corresponding waypoint.

[0011] The instructions may also include instructions for receiving vehicle location coordinates from vehicle sensors and storing the received vehicle sensor data and vehicle location coordinates in a remote computer.

[0012] Also disclosed herein is a method comprising: determining a plurality of waypoints for mapping an area by analyzing received aerial images of the area; selecting an optimal number of waypoints for mapping; and actuating a vehicle to traverse a route determined to cover the selected waypoints.

[0013] Each of the selected waypoints may be at least on a road surface and an intersection.

[0014] The selected waypoints may include a first point and a second point on a road segment, and a curve fitted to the first point and the second point corresponds to the road segment between the first point and the second point.

[0015] The method may also include at least one of generating and updating a map based at least in part on the received LIDAR sensor data, the map covering an area included in the received aerial image.

[0016] The map may include one or more roads and location coordinates of road features including traffic signs and buildings.

[0017] The method may also include routing the vehicle to traverse each of the selected waypoints at least some predetermined number of times.

[0018] The method may further include outputting an image to a display device, wherein the image includes the region having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on a number of traversal times of the corresponding waypoint.

[0019] The method may also include receiving vehicle location coordinates from a vehicle sensor; and storing the received vehicle sensor data and vehicle location coordinates in a remote computer.

[0020] Also disclosed herein is a system comprising: a vehicle; means for determining a plurality of waypoints for mapping an area by analyzing received aerial images of the area; means for selecting an optimal number of waypoints for mapping; and means for actuating the vehicle to traverse a route determined to cover the selected waypoints.

[0021] Each of the selected waypoints is at least on a road surface and an intersection.

[0022] The system may also include means for routing the vehicle to traverse each of the selected waypoints at least some predetermined number of times.

[0023] The system may further include means for outputting an image to a display device, wherein the image includes the region having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on a number of traversal of the corresponding waypoint.

[0024] Also disclosed is a computing device programmed to perform any of the above method steps.

[0025] A computer program product is also disclosed, the computer program product comprising a computer-readable medium storing instructions, the instructions being executable by a computer processor to perform any one of the above method steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a block diagram of an example area and an example satellite providing aerial imagery of the area.

[0027] Figure 2 Shown for mapping Figure 1 Example vehicles in the area.

[0028] Figure 3 Shown is determined in Figure 1 Waypoints in an aerial image of an area.

[0029] Figure 4 Shown is determined in Figure 1 The reduced number of waypoints in the aerial image.

[0030] Figure 5 A hybrid image of a portion of the area is shown, the hybrid image including generated image data that distinguishes waypoints with sufficient data from waypoints with insufficient data.

[0031] Figure 6 A hybrid landscape image including route instructions for mapping a vehicle is shown.

[0032] Figure 7is a flow chart of an exemplary process for navigation mapping a vehicle. DETAILED DESCRIPTION

[0033] System components

[0034] Vehicle navigation may be based on three-dimensional (3D) map data of the area in which the vehicle is operating. A vehicle computer may use a 3D map of the area to determine vehicle location and / or navigate the vehicle within the area. The 3D map may be generated from data collected by driving the vehicle through the area and collecting LIDAR data. Aerial images of the area may be used to manage navigation of a LIDAR data collection device within the area.

[0035] Figure 1An example system 100 for obtaining LIDAR data for 3D mapping in a geographic area 105 is shown. Features of the area 105 may be captured, for example, in an aerial image 110 by a satellite 140. In the context of the present disclosure, a geographic area 105 (or simply area 105) refers to a two-dimensional area on the surface of the earth. The boundaries or edges of the area 105 may be defined by global positioning system (GPS) coordinates, for example, as vertices of a triangular or rectangular area 105, the center of a circular area 105, etc. The area 105 may have any size and / or shape, for example, a rectangle, an ellipse, a circle, a non-geometric shape, etc. For example, the area 105 may include a block, a town, an airport, etc. The 3D map of the area 105 may include features in a volume defined as a bottom side by the area. For example, a 3D map of a rectangular area 105 is a digital map that typically includes features in a rectangular volume defined by a rectangular bottom area 105, and a specified height for performing mapping, for example, 3 meters, 5 meters, 25 meters, etc. above ground level. The 3D map of the area 105 includes location coordinates, such as global positioning system (GPS) coordinates of points within the area 105, e.g., points defining map features such as roads 130, waypoints 120, intersections 160, and altitudes of points within the volume defined by the area 105. In one example, the map may include location coordinates of road 130 features such as traffic signs, buildings, and / or altitudes of points on the outer surface of road 130 features such as buildings, traffic signs, vegetation, etc. In the present context, an image, such as an aerial image 110, is a digital image that includes a plurality of pixels (or points) and data associated with each of the pixels, such as color, intensity, etc. The image may be a "captured" image, i.e., captured by a camera or other optical sensor such as a vehicle camera, a satellite 140 camera, etc., and / or may include "generated" image data, i.e., data generated from stored information such as map data, e.g., a generated line showing a road within the area 105. Additionally or alternatively, the image may be a "hybrid" image, i.e., may include both captured image data and image data generated, e.g., based on mapping data, an image processing algorithm, etc. For example, a hybrid image may include an image of a road with markings superimposed on the captured image data, e.g., based on the output of an image processing algorithm.

[0036] Aerial image 110 is a top view of area 105, for example, an image captured by a camera pointed above the ground. The camera can be mounted on a satellite 140, an airplane, a helicopter, an unmanned aerial vehicle (or drone), a balloon, a stand-alone pole, etc. In one example, field of view 150 of a satellite 140 camera can output aerial image 110 data including area 105.

[0037] Area 105 may include features such as waypoints 120, roads 130, intersections 160, buildings, landscapes, mountains, etc. In the context of the present disclosure, waypoints 120 are points located on the road surface of roads 130 and / or intersections 160, as contrasted with points that are not located on the road surface of roads 130, e.g., points in wooded areas, atop buildings, etc. Waypoints 120 may be designated to support navigation vehicle 180 in mapping area 105, as further described below.

[0038] Two or more roads 130 may intersect. A location or set of points where two or more roads 130 intersect is an intersection 160.

[0039] The road 130 may have various features. The road 130 features are physical properties of the road 130, such as the road 130 pavement material, width, number of lanes, lane markings, one-way or two-way, etc., and / or any physical structures around the road 130, such as guard rails, pathways, traffic signs, traffic lights, one or more buildings, bridges, etc. In this document, the road 130 includes any type of dirt road 130 or a paved road 130 covered, for example, with concrete, asphalt, tar, etc. The road 130 may have one or more lanes. The road 130 may be one-way or two-way.

[0040] In contrast to the aerial image 110, in the context of the present disclosure, a lateral image (e.g., such as Figure 6 ) is a two-dimensional image providing a view substantially perpendicular to the ground, for example, obtained by a camera having an axis substantially parallel to the ground. Alternatively or additionally, the angle between the axis and the ground can be less than 45 degrees. Additionally or optionally, the lateral image 600 can be a hybrid image, that is, it can include both image data captured by the camera and image data generated based on the mapping data and / or data generated by the image processing algorithm, for example, an arrow indicating a planned route.

[0041] The road 130 may include a plurality of road segments 170a, 170b. The road 130 sections 170a, 170b may include two or more waypoints 120. The road segments 170a, 170b are defined by a first waypoint 120a and a second waypoint 120b. For example, the road segment 170a includes all waypoints of the road 130 between the first waypoint 120a and the second waypoint 120b. The road segment 170b may include each waypoint 120 between the second waypoint 120b and the third waypoint 120c. In the context of the present disclosure, crossing, for example, the road 130 section 170a means that the vehicle 180 has traveled from the first waypoint 120a of the road 130 section 170a to the second waypoint 120b of the road 130 section 170a.

[0042] The example vehicle 180 includes a computer 210, one or more drivers 220, sensors 230 such as (Light Detection and Ranging) LIDAR sensors 230, GPS sensors 230, and other components, such as discussed below. The vehicle 180 can be powered in a variety of ways, including, for example, using an electric motor and / or an internal combustion engine.

[0043] Computer 210 includes a processor and memory. The memory includes one or more forms of computer-readable media and stores instructions executable by computer 210 to perform various operations, including as disclosed herein.

[0044] The computer 210 can operate the vehicle 180 in an autonomous mode, a semi-autonomous mode, or a non-autonomous mode. For the purposes of this disclosure, the autonomous mode is defined as a mode in which the computer 210 controls each of the propulsion, braking, and steering of the vehicle 180; in the semi-autonomous mode, the computer 210 controls one or two of the propulsion, braking, and steering of the vehicle 180; and in the non-autonomous mode, a human driver controls the vehicle propulsion, braking, and steering.

[0045] The computer 210 may include programming to operate one or more of vehicle brakes, propulsion (e.g., controlling acceleration of the vehicle 180 by controlling one or more of an internal combustion engine, an electric motor, a hybrid engine, etc.), steering, climate control, interior and / or exterior lights, etc., and determine whether and when the computer 210 (as opposed to a human driver) controls such operations.

[0046] The computer 210 may include or may be communicatively coupled to more than one processor, such as a controller included in the vehicle for monitoring and / or controlling various vehicle controllers, such as a powertrain controller, a brake controller, a steering controller, etc., for example, via a vehicle communication bus as further described below. The computer 210 is typically arranged to communicate over a vehicle communication network, such as a bus in the vehicle, such as a controller area network (CAN) or the like.

[0047] Via the vehicle network, the computer 210 can transmit messages to and / or receive messages from various devices in the vehicle, such as the LIDAR sensor 230, the drive 220, etc. Alternatively or additionally, where the computer 210 actually includes multiple devices, the vehicle communication network can be used for communication between the devices represented in this disclosure as the computer 210. In addition, as described below, various controllers or sensors 230 can provide data to the computer 210 via the vehicle communication network.

[0048] The vehicle 180 driver 220 may be implemented via a circuit, chip, or other electronic component that can actuate various vehicle subsystems according to appropriate control signals as known. The driver 220 may be used to control braking, acceleration, and steering of the first vehicle 180. As an example, the vehicle 180 computer 210 may output control instructions to control the driver 220.

[0049] The vehicle 180 may include one or more LIDAR sensors 130, providing data covering at least some of the exterior of the vehicle 180. The LIDAR sensor 230 may include a processor programmed to transmit LIDAR data via the vehicle 180 network. The LIDAR data may be received by the vehicle 180 computer 210 from the LIDAR sensor 230 in a known manner, for example, via the vehicle 180 network, whereby the LIDAR data may be stored in a memory of the computer 210. The LIDAR data may include, for example, coordinates in a three-dimensional Cartesian coordinate system. The LIDAR data may also include other data relating to other objects, such as size, relative speed to the host vehicle 180, etc. In Figure 2 In one example shown, the LIDAR sensor 230 can scan an area 105, such as an area 105 around the vehicle 180, in the field of view of the LIDAR sensor 230 by emitting a LIDAR beam (e.g., a laser beam) and receiving reflections of the emitted LIDAR beam. In general, the LIDAR sensor 230 can provide data for mapping the physical features of the sensed object with extremely high clarity.

[0050] In addition, the computer 210 can be programmed to communicate with, for example, a remote computer via a wireless communication network. The wireless communication network, which may include a vehicle-to-vehicle (V-to-V) and / or vehicle-to-infrastructure (V-to-I) communication network, includes one or more structures whereby the vehicle 180, a remote computer, etc. can communicate with each other, including any desired combination of wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or multiple topologies when multiple communication mechanisms are used). Exemplary V-to-V or V-to-I communication networks include cellular, Bluetooth, IEEE 802.11, dedicated short-range communications (DSRC), and / or wide area networks (WANs), including the Internet, to provide data communication services.

[0051] As discussed above, the mapping vehicle 180 may traverse the roads 130 within the area 105 to collect LIDAR data to generate a 3D map of the corresponding area 105. Figure 3 to Figure 4, the computer 210 and / or the remote computer may be programmed to: determine a plurality of waypoints 120 for mapping the area 105 by analyzing the received aerial image 110 of the area 105; select an optimal number of waypoints 120 for mapping; and actuate the vehicle 180 to traverse a route determined to cover the selected waypoints 120.

[0052] The route (i.e., the path between the starting location and the ending location) may include a collection of one or more road 130 segments, such as segment 170c. The vehicle 180 computer 210 may be programmed to operate the vehicle 180 to traverse the route. The vehicle 180 computer 210 may be programmed to operate vehicle 180 components, such as propulsion, braking, and / or steering, in an autonomous mode to traverse the route. Additionally or alternatively, the vehicle 180 computer 210 may be programmed to operate the vehicle 180 in a semi-autonomous mode or a non-autonomous mode, as described in reference to FIG. Figure 6 Discussion.

[0053] As discussed above, waypoints 120 are points on the surface of road 130 and / or intersection 160. Figure 3 As shown, the computer 210 may be programmed to determine a plurality of waypoints 120 based on the aerial image 110, for example, using an image processing process. As discussed above, the road 130 includes road segments 170. The road 130 segments 170 are specified by first and second waypoints 120 at first and second ends of the respective road segments 170. Thus, the road 130 may be specified by a plurality of waypoints 120 identified in the aerial image 110. However, specifying the road 130 does not necessarily require every single waypoint 120 identified based on the aerial image 110, as the road 130 may be specified by the position coordinates of an optimal number of waypoints 120, as described below with reference to Figure 4 Discussion.

[0054] The optimal number of waypoints 120 refers to the minimum number of waypoints 120 sufficient to fit a curve whose error does not exceed a predetermined error threshold, e.g., 1 meter, from a midpoint on the side of the road 130. In other words, when the curve is fitted to the optimal number of waypoints 120 on a section of the road 130, then anywhere on the fitted curve, the lateral distance from a point on the fitted curve to the corresponding midpoint on the side of the road 130 does not exceed the predetermined error threshold. Figure 4As shown, the computer 210 may be programmed to identify waypoints 120d, 120h on the road segment 170c and a plurality of other waypoints between the waypoints 120d, 120h. The computer 210 may be programmed to identify an optimal set of waypoints 120d, 120e, 120f, 120g, and 120h and fit a curve on the optimal set of waypoints 120d, 120e, 120f, 120g, and 120h to determine the road 130 segment 170c based on a maximum error threshold (e.g., 1 m). Therefore, the selected waypoints 120d, 120e, 120f, 120g, and 120h may include a first waypoint 120d and a second waypoint 120h on the road 130 segment 170c, and the curve fitted to the first waypoint 120d and the second waypoint 120h corresponds to the road 130 segment 170c between the first waypoint 120d and the second waypoint 120h. “Corresponding” means that anywhere on the fitting curve, the lateral distance of a point on the curve from the center of the road 130 does not exceed a predetermined error threshold, for example, 1 m.

[0055] The computer 210 and / or the remote computer may be programmed to generate and / or update a 3D map based at least in part on the received LIDAR sensor 230 data, the map covering the area 105 included in the received aerial image 110. The computer 210 may be programmed to receive the vehicle 180 location coordinates from the vehicle 180 GPS sensor 230, and store the received vehicle LIDAR sensor 230 data and the vehicle 180 location coordinates in the remote computer. The remote computer may be programmed to generate a 3D map of the area 105 by generating a 3D map including the location coordinates of the road 130 and the road 130 features including traffic signs and buildings. In other words, the 3D location coordinates included in the 3D map may include the GPS location coordinates of the point and the height of the point from the ground.

[0056] The 3D map is generated based on LIDAR data collected by mapping the route of the vehicle 180 through the area 105. The computer 210 can be programmed to determine a route based on the waypoints 120, such as using known optimization techniques, so that the total driving distance of each road 130 traversing the area 105 is minimized. In other words, the computer 210 can be programmed to optimize the time and / or amount of fuel used to traverse each waypoint 120 of the area 105.

[0057] The computer 210 can be programmed to route the vehicle 180 to traverse each of the selected waypoints 120 at least a predetermined number of times (e.g., 3 times). Crossing each waypoint 120 multiple times can provide more data from, for example, buildings, traffic signs, bridges, road 130 boundaries, etc., which can improve the quality of the generated 3D map. Quality in this context refers to the accuracy of the 3D positioning coordinates included in the 3D map.

[0058] exist Figure 5 In one example shown, the computer 210 may be programmed to identify waypoints 120 for which sufficient data has been collected (e.g., traversed 3 times) versus waypoints 120 for which insufficient data has been collected. For example, the computer 210 may be programmed to store a crossing count for each of the optimal number of waypoints 120 and generate an aerial hybrid image 500 showing insufficiently traversed waypoints 510 and sufficient traversed waypoints 520. The computer 210 may be programmed to identify a waypoint 120 as being sufficiently traversed when the crossing count of the corresponding waypoint 120 exceeds a predetermined threshold (e.g., 3) and to identify a waypoint 120 as being insufficiently traversed when the crossing count of the corresponding waypoint 120 is less than a predetermined threshold. The computer 210 may be programmed to output the image 500 to a display device, such as the vehicle 180 HMI 240. The image 500 may include the area 105 with data superimposed on a plurality of waypoints 120, and the superimposed data on each of the waypoints 120 is based on a number of crossings (or counts) of the corresponding waypoint 120. For example, the computer 210 may be programmed to generate a hybrid image, such as the image 500 including the aerial image 110 of the corresponding area 105 and a generated image (e.g., the dashed portion of the image), based on the counts of crossings stored for each waypoint 120.

[0059] The computer 210 may be programmed to generate a route for the area 105 based on the stored crossing counts for each waypoint 120 and a predetermined minimum number of expected crossings (e.g., 3 times). The computer 210 may be programmed to determine the route so that waypoints 120 having crossing counts less than a threshold are included in the route. As discussed above, the computer 210 may be programmed to navigate the vehicle 180 by actuating the vehicle 180 in an autonomous mode to propel, brake, and / or steer to traverse the generated route. In another example, the computer 210 may be programmed to generate, for example, an image 600 based on the generated route, the image 600 including data (such as an arrow) guiding the vehicle 180 superimposed on the road 130. Thus, the vehicle 180 user may navigate the vehicle 180 based on the generated route to collect data from waypoints 120 where insufficient data was collected.

[0060] deal with

[0061] Figure 7 is a flow chart of an exemplary process 700 for navigation mapping a vehicle. The vehicle 180 computer 210 may be programmed to perform the blocks of the process 700 .

[0062] Process 700 begins in decision block 710, where computer 210 determines whether aerial image 110 data is received. Computer 210 may be programmed to receive aerial image 110 data from satellite 140, a remote computer, etc. If computer 210 determines that aerial image 110 data is received, process 700 proceeds to block 720; otherwise, process 700 returns to decision block 710.

[0063] In block 720, the computer 210 determines the waypoints 120 based on the received aerial image 110 data. The computer 210 may be programmed to identify the waypoints 120 in the area 105 included in the aerial image 110 by analyzing the received aerial image 110 data.

[0064] Next, in box 730, the computer 210 selects an optimal number of waypoints 120. The computer 210 can be programmed to identify an optimal number (or set) of waypoints 120d, 120e, 120f, 120g, and 120h, and fit a curve on the optimal set of waypoints 120d, 120e, 120f, 120g, and 120h to determine the road 130 segment 170c based on a maximum error threshold (e.g., 1 m).

[0065] Next, in block 740, computer 210 routes vehicle 180 to traverse the determined waypoints 120. Computer 210 may be programmed to route vehicle 180 to traverse each of the selected waypoints 120 at least a predetermined number of times (e.g., 3 times). Computer 210 may be programmed to route vehicle 180 that includes the entire area 105, i.e., all waypoints 120 within area 105. Additionally or alternatively, computer 210 may be programmed to route vehicle 180 that includes at least waypoints 120 for which insufficient data has been collected.

[0066] Next, in block 750, the computer 210 navigates the vehicle 180 through the determined route. The vehicle 180 computer 210 may be programmed to navigate the vehicle 180 through the path in an autonomous mode by actuating components such as propulsion, actuation, and / or steering. Additionally or alternatively, the vehicle 180 computer 210 may be programmed to operate the vehicle 180 in a semi-autonomous mode or a non-autonomous mode, for example, by outputting instructions to the vehicle 180 HMI 240.

[0067] After block 750, process 700 ends, or alternatively, returns to decision block 710, but Figure 7 Not shown.

[0068] The computing devices discussed herein typically each include instructions that can be executed by one or more computing devices such as those described above and used to perform the blocks or steps of the above-described processes. The computer-executable instructions can be compiled or interpreted from a computer program created using a variety of programming languages ​​and / or technologies, including, but not limited to, Java, alone or in combination. TM , C, C++, Visual Basic, Java Script, Perl, HTML, etc. Typically, a processor (e.g., a microprocessor) receives instructions, such as from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Various computer-readable media can be used to store and transmit such instructions and other data. Files in a computing device are typically a collection of data stored on a computer-readable medium such as a storage medium, a random access memory, etc.

[0069] Computer-readable media include any media that participate in providing data (e.g., instructions) that can be read by a computer. Such media can take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical disks or disks and other persistent memories. Volatile media include dynamic random access memory (DRAM) that usually constitutes main memory. Common forms of computer-readable media include, for example, floppy disks, floppy disks, hard disks, tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, any other physical media with hole patterns, RAMs, PROMs, EPROMs, FLASH-EEPROMs, any other memory chips or cassettes, or any other media that a computer can read from.

[0070] With respect to the media, processes, systems, methods, etc. described herein, it should be understood that although the steps of such processes, etc. have been described as occurring in a particular order, such processes may be practiced with the steps performed in an order other than that described herein. It should also be understood that certain steps may be performed simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of systems and / or processes herein are provided for the purpose of illustrating certain embodiments and should in no way be construed as limiting the disclosed subject matter.

[0071] Therefore, it should be understood that the present disclosure (including the above description and the accompanying drawings and the appended claims) is intended to illustrate, rather than to limit. After reading the above description, those skilled in the art will understand many embodiments and applications other than the examples provided. The scope of the present invention should not be determined with reference to the above description, but should be determined with reference to the claims attached thereto and / or included in the non-provisional patent application based thereon and the full scope of the equivalents of the rights of such claims. It is expected and intended that future developments will occur in the fields discussed herein, and the disclosed systems and methods will be incorporated into such future embodiments. In summary, it should be understood that the disclosed subject matter is capable of modification and variation.

[0072] According to the present invention, a system includes a processor and a memory, the memory storing instructions executable by the processor to: determine a plurality of waypoints for mapping an area by analyzing received aerial images of the area; select an optimal number of waypoints for mapping; and actuate a vehicle to traverse a route determined to cover the selected waypoints.

[0073] According to an embodiment, each of the selected waypoints is at least on a road surface and an intersection.

[0074] According to an embodiment, the selected waypoints include a first point and a second point on a road segment, and the curve fitted to the first point and the second point corresponds to the road segment between the first point and the second point.

[0075] According to an embodiment, the instructions further include instructions for at least one of: generating and updating a map based at least in part on the received LIDAR sensor data, the map covering an area included in the received aerial image.

[0076] According to an embodiment, the map includes one or more roads and location coordinates of road features including traffic signs and buildings.

[0077] According to an embodiment, the instructions further comprise instructions for determining a vehicle route to traverse each of the selected waypoints at least some predetermined number of times.

[0078] According to an embodiment, the instructions further comprise instructions for outputting an image to a display device, wherein the image comprises the region having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on a number of crossings of the corresponding waypoint.

[0079] According to an embodiment, the instructions further include instructions for: receiving vehicle location coordinates from a vehicle sensor; and storing the received vehicle sensor data and the vehicle location coordinates in a remote computer.

[0080] According to the present invention, a method includes: determining a plurality of waypoints for mapping an area by analyzing received aerial images of the area; selecting an optimal number of waypoints for mapping; and actuating a vehicle to traverse a route determined to cover the selected waypoints.

[0081] According to an embodiment, each of the selected waypoints is at least on a road surface and an intersection.

[0082] According to an embodiment, the selected waypoints include a first point and a second point on a road segment, and the curve fitted to the first point and the second point corresponds to the road segment between the first point and the second point.

[0083] According to an embodiment, the above invention is further characterized by at least one of the following: generating and updating a map based at least in part on received LIDAR sensor data, the map covering an area included in the received aerial image.

[0084] According to an embodiment, the map includes one or more roads and location coordinates of road features including traffic signs and buildings.

[0085] According to an embodiment, the above invention is further characterized in that the vehicle route is determined to traverse each of said selected waypoints at least a certain predetermined number of times.

[0086] According to an embodiment, the above invention is further characterized in that an image is output to a display device, wherein the image includes the area having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on the number of crossings of the corresponding waypoint.

[0087] According to an embodiment, the above invention is further characterized by receiving vehicle location coordinates from a vehicle sensor; and storing the received vehicle sensor data and vehicle location coordinates in a remote computer.

[0088] According to the present invention, a system is provided, the system having: a vehicle; means for determining a plurality of waypoints for mapping an area by analyzing received aerial images of the area; means for selecting an optimal number of the waypoints for mapping; and means for actuating the vehicle to traverse a route determined to cover the selected waypoints.

[0089] According to an embodiment, each of the selected waypoints is at least on a road surface and an intersection.

[0090] According to an embodiment, the above invention is further characterized by means for routing the vehicle to traverse each of said selected waypoints at least a certain predetermined number of times.

[0091] According to an embodiment, the above invention is also characterized by a component for outputting an image to a display device, wherein the image includes the area having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on the number of crossings of the corresponding waypoint.

Claims

1. A method for generating a map, the method comprising: determining a plurality of waypoints for mapping the area by analyzing the received aerial image of the area; Selecting the optimal number of waypoints for mapping; as well as actuating the vehicle to traverse a route determined to cover the selected waypoint; The method further includes determining a vehicle route to traverse each of the selected waypoints at least some predetermined number of times; The method further includes outputting an image to a display device, wherein the image includes the region having data superimposed on the plurality of waypoints, and the superimposed data on each of the waypoints is based on a number of traversal times of the corresponding waypoint; The method also includes causing waypoints having a number of traversals less than a threshold to be included in the route.

2. The method of claim 1, wherein each of the selected waypoints is at least on a road surface and an intersection. 3 . The method of claim 1 , wherein the selected waypoints include a first point and a second point on a road segment, and a curve fitted to the first point and the second point corresponds to the road segment between the first point and the second point.

4. The method of claim 1, further comprising at least one of: generating and updating a map based at least in part on received LIDAR sensor data, the map covering an area included in the received aerial image. 5 . The method of claim 4 , wherein the map includes one or more roads and location coordinates of road features including traffic signs and buildings.

6. The method of claim 1, further comprising: receiving vehicle location coordinates from vehicle sensors; as well as The received vehicle sensor data and the vehicle location coordinates are stored in a remote computer.

7. A computing device programmed to perform the method of any one of claims 1 to 6.

8. A computer program product, comprising a computer readable medium storing instructions executable by a computer processor to perform the method of any one of claims 1 to 6.

9. A ground vehicle comprising a computing device programmed to perform the method of any one of claims 1 to 6.

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