Patent Description:
This disclosure relates generally to systems and methods to apply markings to a surface.

Vast sums of money are spent in the U. and throughout the world to apply road markings on various road surfaces. In some examples, such as for longitudinally extending straight and curved lines along the roadway, machines may be used to apply corresponding road markings. In other examples, where more complex shapes and lines are needed, road markings are often applied by hand using stencils. The associated costs with applying such markings are largely dependent upon the personnel required to apply painting as well as to direct traffic near the location where the markings are being applied. Additionally, because stencils are hand painted, workers may be exposing themselves to potential injury from collisions with vehicles or work vans.

To address these and other issues, some automated systems have been developed. It seems that for many applications, however, such automated systems fail to provide practical solutions. For example, there may be intermittent or sustained issues associated with accurately localizing where to apply a given marking. Additionally or alternatively, the approaches may seem too complicated to use by planning and/or field personnel.

Prior art can be found in <CIT> which generally relates to a device for applying road marking, has regulation unit and application unit which is arranged on road vehicle, and positioning unit is connected to data memory for determining actual position and target position of road marking, in <CIT> which generally relates to pavement marking determination and in <CIT> which generally relates to roadway mark data acquisition and analysis apparatus, systems and methods.

In one example, a method includes storing marking data to specify at least one selected marking to apply at a target location along a vehicle path of travel, the marking data including a machine-readable description and a marking reference coordinate frame for the selected marking. The method also includes generating task plan data to apply the selected marking based on the marking data and at least one parameter of an application tool. The method also includes determining a location and orientation of the application tool with respect to the vehicle path of travel based on location data representing a current location of a vehicle carrying the application tool. The method also includes computing a joint-space trajectory to enable the application tool to apply the selected marking at the target location based on the task plan data and the determined location of the application tool.

In another example, a system may apply markings to a surface. The system includes at least one sensor to provide location data representing a current pose of a vehicle carrying an application tool along a vehicle path of travel. One or more non-transitory machine-readable media can store instructions, marking data and task plan data. The marking data describes at least one selected marking to apply at a target location, including a marking reference frame for the selected marking. The task plan data describes a process of applying the selected marking based on at least one parameter of the application tool. A processor may execute the instructions to at least: determine a pose of the application tool along the vehicle path of travel based on the location data, and compute a joint-space trajectory to enable the application tool to apply the selected marking at the target location based on the task plan data and the pose of the application tool. A tool controller is configured to control the application tool to apply the selected marking at the target location based on the joint-space trajectory.

In yet another example, a method includes storing marking data to specify at least one marking that an application tool, which is carried by a vehicle, is to apply at a target location along an application path of travel for the vehicle. The method also includes receiving geospatial coordinate data from a global positioning system device to represent a current pose of the vehicle along the application path of travel. The method also includes sensing fiducials by at least one other sensor along the application path of travel. The method also includes determining fiducial data representing a fiducial coordinate frame for each of the sensed fiducials along the application path of travel with respect to a reference coordinate frame. The method also includes computing a transformation to correlate the fiducial coordinate frame for each of the sensed fiducials along the application path of travel to a spatial coordinate frame for respective fiducials sensed along a previous survey path of travel. The application path of travel is to approximate the survey path of travel. The method also includes determining a pose of the application tool along the application path of travel based on the transformation and the geospatial coordinate data.

As yet another example, a system may apply markings to a surface. The system includes a global positioning system device to provide geospatial coordinate data representing a current pose of a vehicle carrying an application tool along an application path of travel. At least one other sensor is provided to sense fiducials along the application path of travel. One or more non-transitory machine-readable media store instructions and marking data. The marking data describes at least one selected marking that the application tool is to apply at a target location, including a marking reference frame for the selected marking. A processor is provided to execute the instructions to at least: determine a spatial coordinate frame for the fiducials sensed by the at least one other sensor along the application path of travel. The processor further is to compute a transformation to correlate the spatial coordinate frame for each of the sensed fiducials along the application path of travel to the spatial coordinate frame determined for respective fiducials sensed along a previous survey path of travel, the application path of travel to approximate the survey path of travel. The processor further is to determine a pose of the application tool along the application path of travel based on the transformation and the geospatial coordinate data.

This disclosure relates to systems and methods to apply markings to a surface, such as a road or other structure (e.g., bridge, sign, parking lot, and the like), that may reside on or near a path of travel of a vehicle. As an example, an application tool (e.g., a robot) is carried by a vehicle that can be guided to a start location for applying a given marking at a target location along the vehicle path of travel. Attributes of the given marking can be defined by marking data that may be configured in advance of applying the marking. As used herein, the marking may include adding a graphical object (e.g., one or more symbols, words, lines or a combination thereof), removing or changing the surface (e.g., cleaning, sealing or coating, cutting and/or milling) and the like. For example, a user can utilize a planning system, operating on a computing device, which includes a graphical user interface (GUI) to select one or more markings and assign the selected marking to a target location. This can be done in an office or the on-site by a traffic engineer, highway engineer, city planner or the like using simple drag and drop operations afforded by the GUI (e.g., a CAD-style interface). For example, a user can employ the GUI to drag and drop any standard or customized road marking and position it at a desired location along the vehicle path of travel to create a precise project map that can be stored in computer readable memory as the marking data. In an example, the marking data can include machine-readable description of the marking, a reference coordinate frame for the selected marking and a position and orientation of marking that has been selected in response to the user input. Attributes (e.g., size and materials) may be automatically scaled and programmatically linked with the marking data according to the target location where the marking is to be applied.

To facilitate precision localization of the marking, the vehicle carrying the application tool is configured with an arrangement of sensors. In advance of applying the markings, the vehicle can traverse a survey path of travel where one or more markings are to be applied and produce a map of the roadway that is stored as survey data. The survey data may include geospatial coordinates for the path of travel as well as relative localization for fiducials that are distributed along the path of travel (e.g., a roadway or other surface). Such fiducials may include any fixed object or key landmarks, such as trees, signs, fire hydrants, drain covers, curves, existing road markings (e.g., full or partial markings) or other objects having a fixed pose (e.g., position and orientation) with respect to the path of travel. The survey data may be visualized with the GUI provided by the planning system.

As a further example, a corresponding task plan can be generated to define a process for applying the selected marking using a given application tool, though independent of a target location. For example, the task plan is generated based on the marking data (independent of the target location) and one or more parameters of the application tool (e.g., paint head configuration, distance to target and spray nozzle) to apply the selected marking at a zero reference frame. With the marking data and the task plan stored in memory, the vehicle carrying the application tool can then be advanced along an application path of travel (e.g., corresponding to the same path as the survey path of travel). Once the vehicle arrives at or near the target location, such that the application tool is able to reach the target location, the vehicle can be stopped or slowed down. In some examples, guidance may be provided to the operator to stop the vehicle based on global positioning system (GPS) data and/or other sensor data. For example, a computing device is programmed to determine a current pose of the application tool based on location data that is derived from one or more sensors mounted at fixed known locations with respect to the vehicle, as disclosed herein.

After confirming that the target location is within reachability of the application tool, a joint space trajectory is computed to enable the application tool to apply the selected marking at the target location. The joint-space trajectory may be computed based on the task planning data and the pose of the application tool, as disclosed herein. In response to detecting changes in sensor data that affect the location and/or orientation of the vehicle, the joint-space trajectory may be recomputed to provide an adaptive process to account for such detected changes (e.g., in the vehicle pose, or shift its topography).

In some examples, one of the sensors includes a camera that can acquire an image of a surface containing the target location and superimpose a graphical representation of the selected marking at the target location (e.g., rendered as part of an interactive GUI) based on the determined pose of the application tool. The operator can view the superimposition of the selected marking on a display device at the target location to confirm or reject applying the marking at the target location. For example, the GUI can allow the operator to adjust the position and/or orientation of the marking with respect to the target location. Alternatively, the user may move the vehicle to modify the pose of the vehicle and associated application tool, which movement will be reflected in precision localization. After confirming that the target location for the selected marking is satisfactory, the operator can trigger the application of the selected marking (e.g., in response to a user input). In response, a corresponding joint space trajectory can be computed to apply the marking at the target location (e.g., the original or modified target location). The vehicle may be stationary or moving during application of the marking at the target location. The marking may be a new marking applied to a clean (e.g., unmarked) surface or be applied to overpaint an existing marking.

As a further example, systems and methods disclosed herein can utilize sensor fusion to integrate sensor data acquired by multiple sensor modalities. Examples of sensor modalities may include global positioning system (GPS) sensors, LIDAR sensors, camera, precision odometry sensor, speed sensors, sonar systems, steering angle sensor, ground penetrating radar sensor, a gyroscope sensor and inertial measurements (from inertial sensors), and the like. The sensor fusion can aggregate data received from the plurality of sensors to localize the spatial coordinates and orientation of the vehicle to a higher degree of precision than many existing systems. Moreover, the pose of the application tool is readily determined from the vehicle pose since a reference coordinate frame of the tool has a predefined pose with respect to a reference frame of the vehicle. In an example, uncertainty associated with one or more sensors may be updated in real time and used to weight the sensor values utilized by the sensor fusion accordingly. In an example, the sensor fusion may, based on determining that one or more sensors have a high degree of confidence, select such one or more high-confidence sensors to localize the pose of the vehicle while disregarding the data from the other sensors having higher uncertainty (lower confidence). Thus, in some examples, data from a single high-confidence sensor may be used in some circumstances; whereas, in other examples, data from multiple sensors may be used.

The systems and methods disclosed herein thus can achieve accurate application of markings to the road or other surface of interest. Additionally, since the application of markings is implemented by a robot the graphical details and materials used can be expanded beyond those currently being applied by human operators. For example, by automating the task of applying markings enables more eye-catching and more artistic markings, such as may include encodings for autonomous vehicles, ability to paint sponsor logos, and affordability of adding more bicycle lanes and sharing symbols. Moreover, the approach is adaptive to on-the-fly changes that may occur at the target location between the planning phase and the application phase without requiring replanning or reprogramming of the application process. As a result, markings may be applied more with higher precision, more cost effectively and more safely.

<FIG> depicts an example of a system <NUM> to apply markings to one or more target locations. The system <NUM> is demonstrated in <FIG> as being integrated into a vehicle <NUM>. The vehicle <NUM> can be a truck or other vehicle that can traverse the roadway or other surface along which one or more target locations can be identified for applying respective markings. The vehicle <NUM> may be an autonomous vehicle and/or manually driven vehicle. As disclosed herein, the system <NUM> is configured to perform precision localization of the vehicle <NUM> such as to ascertain the position and orientation (i.e., pose) of a vehicle reference coordinate system to within a predetermined accuracy (e.g., less than one inch, such as to within <NUM> or less). The system <NUM> can include a GPS device (e.g., a GPS receiver) <NUM> to provide geospatial coordinates for a reference frame of the vehicle. In some examples, the GPS device <NUM> may provide centimeter precision for the vehicle <NUM> provided that the sensing antenna remains unobstructed by trees, bridges or other objects (e.g., tall buildings) that can interfere with the GPS accuracy.

The system <NUM> includes one or more other sensors <NUM> that may be utilized to sense fiducials along the vehicle's path of travel to enable precision localization. Such fiducials can be any fixed object along the vehicle's path of travel that can be sensed by the sensors <NUM>. For example, fiducials may include existing road markings, trees, telephone poles, fire hydrants, mail boxes, signs, curbs, manhole covers, water-main accesses, gas-line markings, buried cable markings, curbs, grates, speed bumps or the like. Different types of sensors may be utilized to detect different types of fiducials that may be distributed along the path of travel or fiducials associated with the vehicle that vary as a function of vehicle motion. Examples of such other sensors <NUM> include LIDAR, radar, ground penetrating radar, sonar, ultrasonic sensors, wheel encoders, accelerometers, odometry sensors, wheel angle sensors, color camera as well as other sensing modalities that can detect such features that may be detectable along the path of travel. Explicitly shown in the example of <FIG>, is a camera <NUM> (e.g., one or more digital color cameras). The camera <NUM> thus operates to acquire images (e.g., digital color images at a corresponding frame rate) along the path of travel of the vehicle <NUM>. There can be one or more such cameras <NUM> provided on the vehicle <NUM>, such as may be arranged to acquire images below the vehicle, laterally to the vehicle from the passenger and/or driver side, from the front and/or rear of the vehicle. In an example, the camera <NUM> includes a ground-facing camera adjacent an application tool <NUM> and configured with a field of view that includes a zone of reachability for the application tool.

The system <NUM> can include a sensor interface <NUM> that can perform initial sensor processing (e.g., filtering, analog-to-digital conversion, and the like) to provide an aggregate sensor data to a computing device <NUM>. In some examples, the sensor interface may be integrated into the computing device <NUM>. The computing device <NUM> is configured to process the sensor data, including from the GPS <NUM>, camera <NUM> as well as other sensors <NUM>. The computing device is also configured to provide instructions to control the application tool <NUM>. For example, a tool controller <NUM> can be connected with the computing device <NUM> via a connection (e.g., physical or wireless connection) and the computing device can provide commands (e.g., in the form of a joint-space trajectory) to the controller <NUM> that are utilized to apply each selected marking at respective target locations. For example, the application tool <NUM> is implemented as a robot. As one example, the robot <NUM> is an industrial robot, such as a painting robot, that is commercially available from Yaskawa America, Inc. of Miamisburg, Ohio. Additionally or alternatively, other types of application tools may be used in other examples, such as may vary depending on the type of markings to be applied. While the example system <NUM> in <FIG> is demonstrated as including a single application tool <NUM>, in other examples, more than one application tool (e.g., a plurality of robots) may be implemented on the vehicle <NUM> for performing different marking functions, including performing multiple marking functions concurrently.

The computing device <NUM> can be implemented as a portable device that can be carried on a vehicle <NUM>. The computing device <NUM>, for example can include one or more non transitory machine-readable media to store executable instructions and related data. The computing device <NUM> can also include one or more processors for executing the instructions and computing information to enable command instructions to be provided to the controller <NUM>. The example application tool <NUM> includes a tool reference frame <NUM> such as providing two-dimensional coordinate system having an origin at a fixed location with respect to the tool <NUM>. The origin and coordinate system <NUM> also has a predefined location and orientation with respect to a vehicle reference frame <NUM>. Each of the sensors <NUM>, <NUM> and <NUM> can be calibrated to provide sensor information with respect to the vehicle reference frame <NUM>. For example, the computing device <NUM> can compute corresponding transformations for each sensor such that the sensor information is spatially registered with respect to the vehicle reference frame <NUM>.

In some examples, the system <NUM> also includes a marking system <NUM> that can supply materials or other features to the application tool <NUM> for applying the marking at the target location. For example, the marking system <NUM> can include one or more volumes of paint or other coating materials that can be fluidly connected with the application tool <NUM>, such that upon activation of the tool, a controlled amount of marking material is applied to the target location. Additionally, or alternatively, the marking system <NUM> may include sensors (e.g., a sonar or ultrasonic sensor) and signal processing to determine and control a distance between an applicator of the tool and the surface (e.g., road). The marking system <NUM> thus may provide sensor signal or other information utilized by the controller <NUM> to maintain a desired distance during application of each selected marking.

As mentioned, the computing device <NUM> is programmed to execute instructions for performing various functions associated with determining location and programming the tool <NUM>. The computing device includes marking data <NUM> that can be pre-computed for each selected marking that is to be applied. For example, the marking data <NUM> specifies a type of marking that has been selected, size (or scaling of the selected marking) as well as spatial coordinates of a marking reference frame for the target location to which the selected marking is to be applied. Other data associated with application of the marking can also be stored as part of marking data <NUM>. Such other marking data <NUM> can include, for example task plan data, describing a process for the application tool to create the selected marking as a function of the marking reference frame and one or more tool parameters implemented by the tool <NUM> and associated controller <NUM> to apply the marking. As disclosed herein, the target location can correspond to spatial coordinates of a marking reference frame that has been determined based on location data derived from sensor data (e.g., from the GPS <NUM>, camera <NUM> and/or other sensors <NUM>).

In an example, the sensor data corresponds to fused sensor data generated by a sensor fusion function <NUM>. The sensor fusion function <NUM> is programmed (e.g., machine-readable instructions) to receive sensor data from the GPS sensor <NUM> and from one or more other sensors <NUM> and/or <NUM> as the vehicle <NUM> is along the path of travel. As used herein, the path of travel may refer to a survey path of travel which corresponds to the path of travel and trajectory of the vehicle <NUM> as it maps out the locations to which one or more markings will be applied. The path of travel may also correspond to an application path of travel which is the pose of the vehicle <NUM> as it moves along the path for applying the marking at each respective target location defined by the marking data <NUM>. The sensor fusion function <NUM> thus is programmed to fuse the sensor data from sensors <NUM> and/or <NUM> with the geospatial data from the GPS to provide corresponding fused location data representing a precise (e.g., within about <NUM>) current location of the vehicle <NUM>. In examples where sensor fusion <NUM> is enabled, the fusion function <NUM> is programmed to further determine an uncertainty associated with a measure of location accuracy for each of the geospatial data (e.g., from GPS sensor <NUM>) as well as each other sensor data (e.g., from sensors <NUM> and/or <NUM>). A weight value can be assigned to each of the geospatial data and sensor data that are acquired to provide weighted data. As an example, the weighting may be implemented by an extended Kalman filter that implements weighting to the sensors <NUM>, <NUM> and <NUM> that is inversely proportional to the sensing modality measurement uncertainty that is determined for each respective sensor. The weighting further may vary over time as the uncertainty may vary during the sensing process. For example, the measurement uncertainty (e.g., error) of the GPS sensor <NUM> may increase if the GPS sensing is obstructed such as by buildings, trees, bridges, and the like. The sensor fusion function <NUM> further may aggregate each of the weighted sensor data that is acquired to provide the corresponding fused location data. In this way, the position and orientation of the vehicle <NUM> and, in turn, the application tool <NUM> can be determined as a function of the fused sensor data.

A location calculator function <NUM> can be programmed to implement respective transformations to transform corresponding sensor data from each of the sensors <NUM>, <NUM> and <NUM> into a common coordinate reference frame to facilitate precision localization. As an example, the computing device <NUM> is programmed with a transformation for each sensor <NUM>, <NUM> and <NUM> that is calibrated with respect to the vehicle reference frame <NUM>. The transformation thus can be utilized to compute a spatial transformation for fiducials detected by each of the sensors <NUM> and <NUM> into the reference frame <NUM> of the vehicle <NUM> and the location calculator can utilize the transformed spatial coordinates from such sensors to compute an indication of vehicle pose and/or vehicle motion. As a result, by aggregating location information among the respective sets of sensors <NUM>, <NUM> and <NUM>, the location calculator <NUM> can provide a precision estimate of vehicle pose. Moreover, the sensor fusion function <NUM> can utilize the transformed sensor data for providing the fused sensor data, which may be utilized by the location calculator. As mentioned, the precision localization of the vehicle reference frame <NUM> can be further translated to the reference frame <NUM> of the application tool (based on the known spatial geometry between reference frames <NUM> and <NUM>) over the vehicle path of travel.

The computing device <NUM> also includes a marking control function <NUM>. The marking control function <NUM> can include a joint-space trajectory calculator (see, e.g., <FIG>) programmed to compute a joint-space trajectory to enable application tool <NUM> to apply each selected marking at the target location. The marking control function <NUM> computes the joint-space trajectory based on the marking data <NUM> (e.g., the task plan that has been determined for the selective marking) and the determined pose of the application tool <NUM> (e.g., the current pose of tool reference coordinate frame <NUM>). In some examples, the task plan may include multiple sub-process plans associated with the application of a given marking that may involve more than one application tool. As an example, one sub-process plan may be to apply thermoplastic marking materials and another may be to apply heat in order to achieve suitable thermoset bonding to the underlying surface. As another example, one sub-process plan may apply heat to the surface to be coated, and a next sub-process plan to apply a marking material such as paint to the heated surface. The computed joint-space trajectory thus may likewise include multiple joint-space trajectories for operating at the target location according to the multiple sub-process plans associated with the application of each respective marking. The marking control function <NUM> provides the computed joint-space trajectory to the tool controller <NUM>, which controls one or more actuators of the tool <NUM> to apply the marking at the target location. The marking control <NUM> can also control changes to the marking data <NUM> and/or respond to user input instructions entered by an operator to control operation of the tool <NUM>.

In some examples, a marking zone can be determined for the application tool <NUM> and utilized (e.g., by the marking control <NUM>) to control the tool <NUM>. The marking zone defines a spatial region (or volume) of reachability for the application tool <NUM>. When the target location for a selected marking is located within the marking zone of the tool, the tool <NUM> has sufficient reachability to apply at least a substantial portion of the selected marking at the target location. The substantial portion of the selected marking can be determined based on the overall size of the marking relative to the known reachability of the application tool. For example, if a given marking is larger than the zone of reachability for the application tool, the given marking may be divided into multiple marking portions. The vehicle can be moved to a first marking zone to apply one portion and after that has been completed the vehicle may be moved to a second location to apply the next marking portion, and so forth until the entire marking has been applied. For a given marking or portion thereof, the marking control <NUM> can be programmed to determine whether the vehicle location and orientation is within the marking zone. The marking control <NUM> may further generate guidance to inform a user whether or not the vehicle is in the marking zone. The guidance may be in the form of an audible and/or visual alert.

As a further example, after the vehicle is stopped at or near a start location along the path of travel, the computing device <NUM> can generate a graphical representation of the selected marking that is superimposed onto a current camera image that has been acquired (e.g., by a ground facing camera <NUM>) to include the target location. For example, the superimposed image may be visualized on a display within the vehicle passenger compartment. In this way, the display is provided a visualization of the target marking that has been scaled and graphically rendered at the target location (based on localization data determined by the location calculator <NUM>). This affords the user an opportunity to decide whether or not to actually apply the marking with the current orientation at such target location or if the target location and/or orientation should be adjusted.

For example, an adjustment to the target location may include translation and/or rotation of the selected marking with respect to the target location in response to a user input, which provides a modified target location. If the target location and/or orientation are modified, the marking control <NUM> may compute or recompute the joint space trajectory for the selected marking according to the modified target location. If the target location is not adjusted in response to a user input, the user can instruct the computing device <NUM> to proceed with applying the selected marking at the original target location. In response to such user input, the marking control <NUM> can compute the joint-space trajectory (if not already computed) based on the task plan and the current determined pose of the application tool reference frame <NUM>. The controller <NUM> thus employs the joint-space trajectory that has been computed to apply the selected marking at the target location (e.g., the original or modified target location). This process will be repeated for any number of selected markings along the vehicle path of travel based on the marking data <NUM>.

In some examples, such as where a given marking extends beyond the reachability for a single pass by a stationary vehicle, the vehicle may be controlled (e.g., automatically and/or manually by the user) to move along the path of travel. In this example, the location data will update according to a sample rate that sensor data is acquired (e.g., by sensors <NUM>, <NUM> and/or <NUM>) along the path of travel. The updated location data can be applied to recompute the joint-space trajectory provided that the target location is within the zone of reachability for the application tool <NUM>. For example, marking control <NUM> intermittently recomputes a joint-space trajectory at each of the spaced apart locations along the path of travel, which can be provided to the controller <NUM> to control the application tool <NUM> to apply the marking as the vehicle moves along the path of travel. Additionally, corresponding guidance may be provided continually as the vehicle moves along the path of travel to inform the user whether or not the application tool remains within a zone of reachability for applying the selected marking. In some situations, the vehicle <NUM> may advance along the path of travel and stop for application of the selected marking (or a portion thereof). In other examples, the vehicle may continue to move along the path of travel (at a fixed or variable speed) during application of the selected marking.

By way of example, sensors <NUM> and/or <NUM> can be configured to sense fiducials as the vehicle moves along a survey path of travel. Fiducials may be automatically or manually selected based on survey data acquired during a previous mapping run with the vehicle. For instance, the mapping run may involve driving the vehicle <NUM> along the survey path of travel, which is the same path to which the markings are to be applied. As the vehicle moves along such path of travel, the camera <NUM> and other sensors <NUM> can detect fiducials along the survey path of travel. Fiducials can be identified along the survey path of travel automatically or in response to user input selecting fiducials in a GUI during or after the mapping run has been completed. The location calculator <NUM> can analyze each fiducial in a set of identified fiducials to determine a location information describing a fiducial coordinate frame for each fiducial, such as may be localized with respect to the vehicle reference frame <NUM>.

By way of further example, during the application phase, fiducials may be sensed by sensors <NUM> and/or <NUM> as the vehicle <NUM> moves along the application path of travel. For example, fiducials may be recognized near expected fiducial locations specified in the survey data. Location calculator <NUM> determines a corresponding spatial coordinate frame for each fiducial that is identified along the path of travel. The location calculator can compute a corresponding transformation to correlate the spatial coordinate frame for each of the sensed fiducials along the application path of travel with respect to the spatial coordinate frame of the same fiducials previously identified along the survey path of travel. Such transformation thus can be utilized to ensure that the location data representing the pose of the vehicle reference frame <NUM> and tool reference frame <NUM> is determined to a sufficiently high degree of accuracy as it is based on combination of absolute geospatial data (from GPS <NUM>) and relative localization (from camera <NUM> and other sensors <NUM>).

In the example of <FIG>, the system <NUM> includes a power supply <NUM> configured to supply electrical power to the various components of the system. For example, the power supply can include a generator or other source of electrical power (e.g., an inverter, on-board vehicle power supply or the like). The system may also include a wireless network interface <NUM> to enable communication with a remote device or server (e.g., for monitoring or reporting data acquired during mapping or application phases). For example, the wireless network interface <NUM> can be implemented to communicate digital data via a wireless communications link, such as a Wi-Fi and/or cellular data link.

As a further example, <FIG> depicts an example of a system to generate a survey data <NUM> that represents a path of travel that has been mapped out as a prospective recipient of one or more markings that are being applied. The system <NUM> utilizes data from one or more sensors that can be mounted in a fixed position with respect to a vehicle (e.g., sensors <NUM>, <NUM> and <NUM> of <FIG>) to provide corresponding sensor data <NUM>. In this example, it is presumed that the data <NUM> has been acquired and stored in memory (e.g., one or more non-transitory machine-readable media). For example, the data can be transferred from local storage on the vehicle to another computing device (e.g., via wireless network interface <NUM> or another mechanism, such as a removable storage medium). In another example, the same computing device (e.g., device <NUM> - a laptop or other portable computer) can be used to acquire and store the data <NUM> on the vehicle as well as implement the system <NUM>.

In this example, the sensor data includes GPS data <NUM>, LIDAR data <NUM>, camera data (e.g., image data) <NUM>, odometry data <NUM>, speed data <NUM>, sonar data <NUM>, and steering angle data <NUM>. It is understood that the sensor data <NUM> can use various combinations of the data shown in <FIG> to provide sufficiently precise location related information to generate the survey data <NUM>. The data <NUM> further may be pre-processed and/or otherwise associated with other data, such as synchronized according to a time stamp. Thus, the data <NUM> can represent various attributes of a vehicle and/or surrounding environment along the path of travel.

The system <NUM> includes a vehicle location calculator <NUM> that is programmed to produce location data based on analysis of the sensor data <NUM>. As used herein, the location data can represent the pose of the vehicle along one or more paths of travel. The location calculator <NUM> thus can produce the location and sensor data <NUM> corresponding to the pose of a vehicle reference frame (e.g., reference frame <NUM> of <FIG>). Some or all of the sensor data <NUM> may also be included with the location and sensor data <NUM>. As described herein, such sensor data can be transformed into the coordinate frame of the vehicle to facilitate sensor fusion <NUM> and localization <NUM> in a common reference frame.

To increase localization accuracy based on the sensor data <NUM> that has been obtained from multiple sensor modalities, location calculator <NUM> includes a sensor fusion function <NUM>. Sensor fusion function <NUM> is programmed to determine an indication of accuracy of each of the sensor data, which accuracy may vary over time. For example, in some situations GPS data <NUM> may provide precision approaching about one centimeter provided the sensor has a clear unobstructed view of the sky containing the GPS satellites. However, in certain situations, such as in tree covered areas and in highly dense urban areas with tall buildings, bridges and/or other structures, the precision of the GPS data <NUM> may become less precise. Sensor fusion function <NUM> thus utilizes sensor weighting function <NUM> to selectively weight sensor data according to the determined uncertainty associated with each unit of sensor data <NUM> to facilitate accurate localization of the vehicle. For example, sensor weighting function <NUM> may be implemented as a Kalman filter configured to determine uncertainty and apply weighting coefficients to control the impact provided sample of the data <NUM> - <NUM>, respectively. In this way, the sensor fusion <NUM> can increase the relative influence of sampled sensor data that is determined to have a greater amount of certainty on the location calculation by calculator <NUM> for each sample time instance, while reducing the influence of more uncertain data. As one example, sensor fusion <NUM> implements sensor weighting function <NUM> so that GPS data <NUM> is utilized when precision is determined to be sufficiently high, but utilizes one or more other sensor data <NUM>-<NUM> (e.g., precision odometry data <NUM>, LIDAR data <NUM>, camera data <NUM> and/or other sensors, such as inertial sensors data, gyroscope data and ground penetrating radar data), which are determined to be sufficiently high accuracy, to compute changes in vehicle pose (e.g., motion) with respect to the high precision GPS updates when available along the path of travel.

In an example, the sensor fusion function <NUM> evaluates the weighting values (representing uncertainty of sensor measurements), to identify a set of sensors having a low degree of uncertainty (e.g., below an uncertainty threshold individually or collectively. Alternatively, sensor fusion can determine sensors having a high degree of confidence (e.g., above a defined confidence threshold). The sensor fusion function <NUM> thus can select such one or more high-confidence sensors to use for localizing the pose of the vehicle and/or application tool, while discarding data from the other sensors determined to have greater degree of uncertainty (lower confidence). Consequently, in some examples, sensor fusion function <NUM> can generate fused location data from a single high-confidence sensor and, in other examples, data from multiple sensors may be used. The number of sensors used over the path of travel thus may vary according to changes in the uncertainty associated with each of the sensors.

Sensor fusion function <NUM> can also include a transformation calculator <NUM>. The transformation calculator <NUM> is configured to translate sensor data from a sensor reference frame into vehicle reference frame along the path of travel. That is the reference frame of each sensor is known a prior with respect to the vehicle reference frame. Accordingly, the transformation calculator is programmed with transformations to reconcile the relative measurements provided in each sensor data <NUM>-<NUM> with corresponding absolute coordinates associated with the vehicle reference frame, which may be derived from the GPS data <NUM> and/or from the results of previous calculations.

By way of example, LIDAR data <NUM> includes range and azimuth data (polar coordinates). Since the reference frame of the LIDAR sensor is known relative to a reference frame of the vehicle, the transformation calculator <NUM> is programmed to apply a coordinate transformation to convert the polar LIDAR data <NUM> to corresponding Cartesian coordinate data. The LIDAR data can be analyzed (manually and/or automatically) to identify fiducials along the path of travel, which may be identified as a step change from large radii (no objects returning a signal within range of the LIDAR) to distinctly smaller radii (e.g., a telephone pole reflecting a LIDAR ping). By scanning the LIDAR data for such discontinuities (equivalently, gradients), a set of fiducials and their relative location along the path of travel can be determined. For example, the transformation calculator can compute the pose of the LIDAR sensor that would reconcile the relative measurements (LIDAR-based features) with the corresponding absolute coordinates: <MAT> where T is a 4x4 coordinate transformation,.

Therefore knowing T_feature/world and T_feature/sensor allows computation of T_sensor/world, which can represent a high-precision lattitude and longitude of the LIDAR sensor. With the sensor calibrated with respect to the vehicle, this calibration can be expressed as T_sensor/vehicle, i.e. the pose of the sensor with respect to a reference frame associated with the vehicle. It follows that: <MAT>.

Therefore, knowing T_sensor/world and T_sensor/vehicle, the transformation calculator can compute T_vehicle/world, which corresponds to the absolute (geospatial) coordinates of the vehicle reference frame.

The above example for the LIDAR data <NUM> can be extended and modified to provide corresponding transformations for the other sensor data <NUM>-<NUM>. For example, the camera data <NUM> can acquire images of the road, verge areas adjacent to the road, as well fiducials within the field of view. As with the LIDAR sensor, the transformation calculator <NUM> is programmed to correlate a reference coordinate frame of the camera to the vehicle's reference frame. Through this transform, fiducials in camera coordinates can be converted to fiducials in the vehicle coordinate frame.

For the example where the sensor data includes LIDAR data <NUM>, camera data <NUM> and odometry data <NUM>, the transformation calculator performs three different computations for T_vehicle/world: one from GPS+odometry, one from LIDAR and one from vision. Different numbers and types of computations would be used for different combinations of sensors. As mentioned, since each of these modalities has an associated uncertainty, respective sensor weighting <NUM> is applied to each transformed sensor data to provide the fused location data. The sensor fusion function <NUM> thus can combine the transformed sensor data algebraically based on weightings that are proportional to credibility. For example, a location vector, L, includes estimates from GPS/odometry (L_gps), from LIDAR (L_lidar), and from camera (L_image). In an example, the fusion function <NUM> thus may combine the location estimates as: <MAT> where a+b+c = <NUM>, and a, b and c are weighting values inversely proportional to the modality measurement uncertainty.

Vehicle location calculator <NUM> also includes a precision localization function <NUM> that is programmed to determine vehicle location data representing the pose of a reference coordinate frame of the vehicle based upon the sensor fusion <NUM>. Location data <NUM> thus provides an indication of the vehicle pose along the path of travel of the vehicle during the mapping phase. Corresponding sensor data can also be stored in conjunction with the location data along the path of travel to facilitate generation of the survey data <NUM>. For example, such sensor data can include raw sensor data or processed sensor data that is been transformed (by transformation calculator <NUM>) into the reference frame of the vehicle along the path of travel, as described above.

A survey data generator <NUM> is programmed to generate the survey data <NUM> based on location data and sensor data <NUM>. For example, the survey data generator <NUM> includes a fiducial selector <NUM> that is programmed to select one or more fiducials along the vehicle path of travel based on sensor data (e.g., sensor data <NUM>-<NUM>) from one more sensors. As mentioned, fiducials can correspond to landmarks or other stationary objects that can provide an additional frame of reference to enable precision localization of the vehicle during an application phase when one or more markings are to be applied. The fiducial selector <NUM> thus can identify one or more fiducials based on the sensor data detected along the vehicle's path of travel. Fiducials may be detected automatically from the sensor data such as by signal processing techniques.

For example, camera data <NUM> may be analyzed (e.g., by image or vision processing) over time to segment the images, recognize and extract known fiducials along the vehicle path. In other examples, the fiducial selector <NUM> may provide a graphical user interface that can display a graphical image that has been acquired (e.g., based on camera data <NUM> and/or LIDAR data <NUM>) and present a visual representation on a display device. A user thus can employ a user input device (e.g., mouse or touch screen) to provide a user input for selecting portions of the sensor data to identify one or more objects as fiducials.

The location and sensor data <NUM> generated by the location calculator <NUM> along the path of travel can be utilized to augment or generate map data <NUM>. The map data, for example may correspond to a geospatial map that is generated based on the location data determined by the location calculator based on the sensor data <NUM> acquired along the path of travel. Additionally or alternatively, the map data <NUM> may include a geographic information system (GIS) that is designed to capture, store, manipulate, analyze, manage, and present spatial or geographic datamap information (e.g., web mapping service, such as Google Maps, OpenStreetMap or the like).

Based on the selected fiducials (by fiducial selector <NUM>) and the map data <NUM>, the survey data generator <NUM> provides corresponding survey data <NUM>. The survey data can include path data <NUM> specifying spatial coordinates along the path of travel for the vehicle reference frame. The survey data <NUM> also may include fiducial data <NUM> representing the selected fiducials along the survey path of travel provided by the path data <NUM>. The fiducial data <NUM> thus can include a spatial coordinate frame of each sensed fiducial that has been determined with respect to the vehicle reference frame along the target path and defined by the path data <NUM>.

<FIG> depicts an example of a marking system <NUM> that can be utilized to generate marking data <NUM>. The marking system <NUM> includes a marking generator <NUM>. The marking generator <NUM> can generate the marking data <NUM> to specify one or more selected markings that are to be applied at respective target locations along the survey path of travel. The survey path of travel can be specified in survey data <NUM>. The survey data <NUM> can include path data <NUM> and fiducial data <NUM>. In an example, the survey data <NUM> is generated by survey system <NUM> of <FIG>. In another example, the survey data <NUM> can be provided by another source, such as a GIS that includes a dataset for geospatial coordinates along the survey path of travel. In some examples, survey data <NUM> acquired for a user-specific path of travel is combined with a GIS dataset to enable the marking generator to apply markings to target locations.

For example, the path data <NUM> defines geospatial coordinates of a vehicle reference frame along the survey path of travel. The geospatial coordinates can be determined based on the sensor data and corresponding sensor fusion disclosed herein (e.g., including sensor weighting and sensor spatial transformations). Fiducial data <NUM> can represent locations of identified fiducials along the path of travel (associated with sensor data) as well as a corresponding reference frame relative to the path of travel of the vehicle.

In one example, marking template data <NUM> can provide templates for a plurality of different types of markings <NUM>, demonstrated as marking <NUM> through marking N, where N is a positive integer denoting the different types of markings. The marking generator <NUM> includes a marking selector <NUM> to select one or more markings for placement along the vehicle path of travel. The marking generator also may include a marking GUI <NUM> to enable a user, in response to a user input, to select and position a selected marking at a target location within a visualization of the survey path of travel, that is presented on a display device <NUM>. The marking selector <NUM> further may utilize the marking GUI <NUM> to graphically position a GUI element for given marking <NUM> at a desired target location on the display <NUM>.

A marking coordinate calculator <NUM> is configured to compute a pose (e.g., spatial coordinates and an orientation) of the target location for each selected marking. For example, the marking coordinate calculator <NUM> can compute a marking reference frame for each selected marking having geospatial coordinates (e.g., a position and orientation) with respect to the vehicle path of travel defined by the path data <NUM>. The marking reference frame has defined pose with respect to the target location. A user can adjust the coordinates by selectively moving the selected marking on the marking GUI <NUM> in response a user input (e.g., via mouse or keyboard). The size and other attributes (e.g., marking color, materials or the like) can also be adjusted by the user. In response to a user selection, the selected marking and its associated reference frame can be assigned a given pose (position and orientation) and stored as a part of the marking data. The process may be repeated along the vehicle path of travel until a full set of markings has been assigned for the survey path of travel. The resulting marking data <NUM> specifies each marking that is to be applied and each respective target location along the path of travel. The marking data <NUM> also may store corresponding fiducial data that has been associated with the path data and is stored as part of the survey data. In this way, the marking data <NUM> can include a selected subset of fiducials from the fiducial data <NUM> adjacent target locations along the path of travel as well as target locations from the path data <NUM> to facilitate localization of the vehicle and application tool at each respective target location as disclosed herein.

<FIG> depict a simplified example of a graphical user interface <NUM> (e.g., corresponding to marked marking GUI <NUM> of <FIG>). Thus in the example of <FIG>, an intersection between West Street and North Street is visualized in a graphical map. The map can be generated on a display based on survey data <NUM> and/or map data <NUM> of <FIG>. In this example, North Street runs vertically in the page while West Street runs in a horizontal direction with respect to the page orientation of <FIG>. A set of marking templates <NUM> (e.g., corresponding to marking template data <NUM>) is shown along the edge of the graphical map <NUM>. In this example, the templates <NUM> include various potential road markings that may be selected in response to a user input. The templates include attribute data that define features (e.g., size, color, thickness, etc.) for each selected marking, such as may be user configurable and/or be assigned automatically upon selection.

In the example of <FIG>, a left turn arrow marking has been selected, demonstrated at <NUM>, in response to a user input via a pointer GUI element <NUM>. A user thus may employ the pointer <NUM> to drag and drop the selected marking <NUM> to a desired target location on the graphical map <NUM>. Thus, as shown in <FIG>, the left turn arrow has been dragged from the template panel <NUM> onto a left turn lane of North Street, demonstrated at <NUM>. A user may adjust the location relative to the illustrated roadway, as disclosed herein. In response to placement of the marking at a given location, a corresponding set of marking data for the selected marking may be generated (e.g., by marking generator <NUM>) and stored in memory. In an example, such as where no user adjustment is made, the GUI can be programmed to automatically place the selected template at a default target location, such as by "snapping" the selected template into place in the center of the left turn lane at an appropriate distance from the stop line.

In addition to geospatial coordinates of the selected marking, the marking data <NUM> may also include one or more fiducials. For example, sensor data corresponding to a fire hydrant <NUM> can be stored as part of the marking data to facilitate localization and placement of the selected marking at the target location along an application path of travel for the vehicle. Sensor data for the fire hydrant, for example may include LIDAR data and/or camera data. In this way, if the pose of the vehicle may differ in application phase from the mapping phase (e.g., due to errors), appropriate transformations and sensor fusion may be applied to sensor data (e.g., data <NUM>) to compute the pose of the application tool. In this way, the application tool can be precisely localized such that the differences between the application phase and survey phase may be accounted for in computing the joint-space trajectory for applying the selected marking at the target location.

<FIG> depicts an example of a system <NUM> that includes a location calculator <NUM> configured to ascertain vehicle pose data <NUM>, such as corresponding to a reference frame of the vehicle (e.g., frame <NUM>). Since the pose of the application tool is known a priori with respect to the vehicle, the pose of the application tool is readily determined from the vehicle pose. Accordingly, the approach implemented by location calculator <NUM> of <FIG> can likewise be used to determine pose of the application tool.

The system <NUM> includes a vehicle location calculator <NUM> that is configured to determine the vehicle pose data <NUM> based on sensor data <NUM> and survey data (e.g., survey data <NUM> provided in <FIG>). The vehicle pose data <NUM> thus can provide current (e.g., real-time) pose data <NUM> for the vehicle along an application path of travel. The pose data <NUM> can be defined by a combination of global geospatial coordinates and relative local spatial coordinates along the vehicle path of travel. As discussed with respect to <FIG>, the survey data <NUM> thus can include path data <NUM> and fiducial data <NUM>. The path data <NUM> can represent a trajectory of a reference coordinate frame of the vehicle along the path of travel. The fiducial data <NUM> can correspond to coordinates of various fiducials along the path of travel. For example, the fiducial data <NUM> can be a selected subset of fiducials along the path of travel, which may be selected (e.g., by fiducial selector <NUM>), as disclosed herein.

The system <NUM>, which may be implemented in the computing device on the vehicle (e.g., computing device <NUM>) includes the plurality of sensors that provide the corresponding sensor data <NUM>. For sake of consistency, the sensor data is the same as sensor data in <FIG>. In other examples, different sensors and data may be used for mapping and application location determination. As disclosed herein, in some examples, the sensors may include a GPS sensor <NUM> and one or more other sensors. In other examples, a full complement of sensors may be utilized. In this example, the sensors include a GPS sensor <NUM> that provides GPS data <NUM>, a LIDAR sensor <NUM> that provides LIDAR data <NUM>, a camera sensor <NUM> that provides camera data <NUM>, an odometer <NUM> that provides odometry data <NUM>, a speed sensor <NUM> that provides speed data <NUM>, a sonar sensor <NUM> that provides sonar data <NUM>, and a steering angle sensor <NUM> that provides steering angle data <NUM>. In addition or as an alternative, other sensors may be utilized, such as inertial sensors, ground penetrating radar, or the like. The location calculator <NUM> is configured to access each of the data <NUM> that is provided by the respective sensors.

The vehicle location calculator <NUM> includes a sensor fusion function <NUM> and a precision localization function <NUM>. For example, the sensor fusion function <NUM> may be an instance of the same sensor fusion function <NUM> as discussed with respect to <FIG> and reference may be made back to <FIG> for additional information. Briefly, the sensor fusion function includes a sensor weighting function <NUM> and a transformation calculator <NUM>. The sensor weighting function <NUM> is programmed to determine an uncertainty (e.g., error) associated with sensor data that may vary over time and topography along the path of travel. The weighting function <NUM> selectively weights the each unit of sensor data <NUM> based on a determined uncertainty associated of the respective data to facilitate accurate localization of the vehicle. For example, sensor weighting function <NUM> may be implemented as a Kalman filter configured to weight the respective sensor data <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>. In this way, the sensor fusion <NUM> can increase the relative influence of sensor data that is determined to have a greater amount of certainty on the location calculation by calculator <NUM> for each sample time instance, while reducing the relative influence of more uncertain data.

The transformation calculator <NUM> is programmed to apply spatial transformations to convert sensor data <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> from a sensor reference coordinate frame into the vehicle reference frame along the path of travel. Accordingly, the transformation calculator provides transformed data that is normalized and provided in a common coordinate system to facilitate location computations by the location calculator <NUM>.

The precision localization function <NUM> is configured to determine vehicle location and orientation based on the fused location data that has been transformed into the vehicle reference frame. Such fused location data derived from multi-modal sensors provides global (absolute) geospatial coordinates as well as local (relative) location information. As a result of the precision localization function <NUM> leveraging both absolute and relative location information in the fused location data, a higher level of accuracy can be maintained for the resulting pose data <NUM> along the path of travel.

For example, the precision localization function <NUM> utilizes the survey data <NUM>, which includes the path data <NUM> and the fiducial data <NUM>. The fiducial data <NUM> can include data identifying a selected subset of fiducials detected by respective sensors along with pose (position and orientation) for its respective fiducial reference frame, which has been transformed into the vehicle reference frame. Thus, by matching fiducials described in the survey data with fiducials in like sensor data, the precision localization function can quantify differences to help determine where each target location is in absolute coordinates with respect to the application tool.

For example, the precision localization function <NUM> can implement a fiducial recognition <NUM> to identify and extract fiducials from the corresponding sensor data (e.g., data <NUM>, <NUM> and <NUM>). The fiducial data <NUM> further may be used to specify expected fiducial locations. The pose of extracted fiducial may be evaluated with the pose of fiducials specified in the fidicual data <NUM>. For example, a fiducial frame transformation function <NUM> is programmed to compute a spatial transform relating the pose of each currently sensed fiducial with respect to its previously identified fiducial from the fiducial data <NUM>. For example, the transformation can involve translation in one or two directions (e.g., x or y directions) and/or rotation about the Z axis. Examples of approaches that can be utilized to determine the fiducial transformation can include iterative closest point or particle filtering methods. Other mathematical methods may be utilized in other examples.

In this way, the precision localization <NUM> can use recognized fiducial locations as provided by fiducial data <NUM> along the vehicle path to generate the pose data <NUM> with increased precision, since it is adjusted based on detecting differences between fiducial pose in the fiducial data <NUM> and the weighted and transformed current sensor data <NUM>. Fiducial data thus may be provided during the application phase by any number of sensors that can be aggregated based upon the sensor weighting and corresponding transformations provided by the sensor fusion function <NUM>.

By way of further example, the precision localization function <NUM> can employ transformation function <NUM> to compute the pose of the vehicle (or the application tool) with respect to a given reference frame. For example, if TfidN/cam expresses the position and orientation of the Nth fiducial coordinate frame with respect to the coordinate frame of camera sensor <NUM>, The transformation <NUM> can compute: <MAT>.

A similar transform may be computed for other sensors.

For localization during the application phase, an approximation of the vehicle pose and/or application tool will be calculated and updated along the path of travel, and based on its pose and the fiducial data <NUM>, the fiducial recognition function <NUM> can have an expectation of what fiducials may be detectable. For example, given an image of an expected fiducial while the vehicle is within a distance of the fiducial (e.g., specified in the survey data <NUM>), the transformation <NUM> can compute the corresponding TfidN/cam using image processing. However, the survey data <NUM> generated from the previous mapping run (and post processing) may establish the coordinates of such fiducial N to be TfidN/<NUM>. The transformation function <NUM> thus can compute the reference frame of the application tool, such as follows: <MAT>.

As a result, using sensor processing to match new and previously detected fiducials, the precision localization function <NUM> can compute the pose of the vehicle and/or tool precisely with respect to fiducials. Since the fiducials are pre-mapped such that their coordinates are known with respect to a reference frame (in fiducial data <NUM>), the precision localization function <NUM> can, in turn, compute the pose of the application tool with respect to the same reference frame.

For example, incremental motion of the vehicle may be estimated along the path of travel based on other sensor data acquired by the at least one other sensor along the application path of travel from a first location to a second location. Thus, the pose of the application tool can be updated based on the estimated incremental motion (estimated from the other sensor data) along the portion of the application path of travel between the first location and the second location. In some examples, the first and second locations correspond to the pose of respective first and second fiducials detected along the path of travel. In other examples, the locations can be geospatial coordinates of the vehicle (or application tool). Each of the locations may be derived from sensor data from a single sensor or from fused sensor data determined (e.g., by sensor fusion function) from multiple sensors, as disclosed herein.

As a further example, each time a fiducial from one or more of the sensors is recognized and processed, the corresponding vehicle pose data <NUM> can be updated accordingly. As an example, if the reference frame of the application tool starts at a known pose (e.g., having originally recognized a fiducial from the sensor data corresponding to a known fiducial <NUM>, incremental motion from the starting pose can be estimated from other sensor data (e.g., wheel encoders, steering angle data, accelerometer data, precision odometry, speed sensor data, ground penetrating radar data, gyroscope data, inertial sensor data, LIDAR and the like) that can be compared to the pre-mapped fiducial data and path data <NUM>. Thus, when GPS data may have uncertainty its location may be augmented from location transformations determined for other sensor data, including fiducials detected from such other sensor data. Even though computing such incremental motion from a known reference pose may gradually accumulate localization uncertainty errors, as the other sensor data is acquired, including fiducials that are recognized (e.g., by fiducial recognition function <NUM>) along the vehicle path of travel based on corresponding sensor data <NUM> and spatial transforms computed, such localization uncertainty may be mitigated.

<FIG> illustrates an example of a fiducial transformation that may be implemented. In <FIG>, a pair of fiducials <NUM> and <NUM> is shown. For example, a fiducial <NUM> corresponds to an image that is has been selected and stored in survey data <NUM> (fiducial data <NUM> and the path data <NUM>). The other fiducial <NUM> corresponds to the same fiducial captured by sensor data <NUM> (e.g., camera data <NUM>) as recognized by fiducial recognition function <NUM>. Fiducial transformation function <NUM> can compute a spatial transform from the pose of the second fiducial <NUM> to pose of the first fiducial <NUM>, such as described above. This transformation can include translation and/or rotation of the fiducial corresponding to a distance (e.g., Euclidean or other distance calculation) that the reference frame of image <NUM> must move to align the references frames of respective fiducials <NUM> and <NUM>. Since each sensor reference frame is known with respect to the vehicle reference frame, corresponding spatial coordinates of the vehicle can be ascertained as disclosed herein. Similarly, since the application tool's reference frame is known relative to the vehicle reference frame, the corresponding transformation may further be adjusted to ascertain the pose of the application tool reference frame to the same precision.

<FIG> depicts an example of a system <NUM> that can be implemented to control application of markings to target locations. The system <NUM>, for example can be implemented by the system <NUM> that is integrated into the vehicle <NUM>. In other examples, some of the parts of the system <NUM> may be integrated into a computing device that is carried on a vehicle whereas others may be implemented in a distributed computing arrangement, such as in a cloud or at a server that may be separate from a vehicle. For example, the computing system on a vehicle may employ a wireless network (e.g., via network interface <NUM>) that can access data and functions implemented remotely. In the following example, however, it is assumed that the computing device on the vehicle is configured to implement the controls for using the application tool <NUM> to apply one or more markings at target locations.

The system <NUM> includes a joint-trajectory calculator <NUM>. The joint-trajectory calculator is configured to compute joint-trajectory data <NUM> based on task plan data <NUM> and tool pose data <NUM>. As mentioned, the tool pose data <NUM> can define the spatial coordinates and orientation of a reference frame of the application tool. The tool pose data <NUM> can be determined by a precision localization function as disclosed herein (see, e.g., <FIG>). For example, a tool pose calculator <NUM> can convert the vehicle pose data <NUM> into the tool pose data by applying a corresponding transformation based on the known location and orientation of the tool reference plan relative to the vehicle reference frame.

A task plan generator <NUM> is configured to generate the task plan data based on the marking data <NUM> and to a parameter data <NUM>. While the task plan generator is shown as part of the system <NUM>, in some examples, the task plan may be implemented as part of the system <NUM> of <FIG>. The marking data <NUM>, for example, corresponds to marking data that is generated by marking generator <NUM> of <FIG>. The marking data <NUM> thus can identify the selected marking, as well as geospatial coordinates and orientation of a marking reference frame thereof. Based on the marking data <NUM> and tool parameter data <NUM>, the task plan generator <NUM> can derive a task plan, to define a process path that is executable by the application tool to apply the marking independent of tool location. The tool parameter data <NUM>, for example, may specify a distance between a spray head and the surface to apply the marking, a width of the spray at such distance and other parameters to apply the selected marking by the tool <NUM>. In this way, the task plan data <NUM> provides a set of instructions that can be executed by the application tool to apply the selected markings in Cartesian space, which is independent of the specified target location and pose of the application tool <NUM>.

The joint-trajectory calculator <NUM> thus computes the joint-trajectory data <NUM> to include corresponding instructions to enable the application tool <NUM> to apply the selected marking at the target location based on the task plan data <NUM> and current tool pose data <NUM>. For example, the joint-trajectory calculator <NUM> implements inverse kinematics to map the task plan for the selected marking in Cartesian space into joint space of the application tool. The particular mapping and joint-space trajectory will depend on the configuration of the application tool (e.g., number of joints, actuators, length of arms and the like).

As an example, the vehicle is utilized to position the robot to an estimated location, which yields a current tool pose. The joint-space trajectory calculator <NUM> is programmed to employ inverse kinematics on the task plan for the selected marking and based on the actual pose of the application tool <NUM> to derive a set of instructions (data <NUM>) in the tool's joint space to apply the selected marking at the desired target location within a desired level of precision. In this way, despite being displaced from the nominal coordinates for applying the selected marking at the target location, the joint-space trajectory data <NUM> compensates for the difference in tool pose from target location to ensure that the selected marking is applied at the desired target location. A tool control system <NUM> thus interprets the joint-space trajectory data <NUM> into a series of instructions for controlling the application tool <NUM> for applying the marking at the desired target location.

Since the application tool <NUM> is capable of applying marking at coordinates with respect to its reference frame over a corresponding reachability zone, the pose of the application tool <NUM> must be within a corresponding zone of reachability to enable the selected marking to be applied at the target location. Accordingly, the system <NUM> may include a reachability analyzer <NUM> to ascertain whether the tool pose is within the zone of reachability provided by the target location in the marking data <NUM>. The reachability analyzer <NUM> can provide guidance to a marking user interface <NUM>. For example, the marking user interface <NUM> can provide guidance (e.g., audible and/or visual guidance) to a user. The guidance can indicate whether or not the current tool pose is sufficiently within the zone of reachability to enable the application tool <NUM> to apply the marking (or at least a substantial portion thereof) at the target location. Thus, by positioning the application tool (e.g., painting robot) at an approximation to a desired pose, the system <NUM> is configured to transform the desired marking coordinates to a joint-space trajectory to accommodate the actual pose of the robot relative to target location on the surface. In this way, the robot can be displaced from nominal coordinates yet continue to apply markings precisely where desired on the surface.

In some examples, the marking user interface <NUM> can receive a user input response to instructions from a user input device (e.g., mouse, keypad, touch pad, touch screen or the like). For example, the instructions may include confirmation by the user to begin the marking process and apply the selected marking at the target location. In another example, the marking user interface <NUM> may be implemented as a GUI that displays a graphical representation of the selected marking at the target location that has been calculated. The user can view the selected marking superimposed on an actual image (e.g., from surface facing camera) that is presented on a display device of the computing device. Based on the image showing where the marking will be applied, the user may make a more informed decision about whether to confirm or reject applying the marking at such location. If the user rejects the application at the current target location, the marking user interface <NUM> may further present a GUI to enable the user to graphically adjust the target location relative to the displayed camera image in response to a user input. If the user adjusts the target location, an adjusted target location may be provided and stored as the marking data <NUM>. The adjusted target location can in turn be provided to the joint-space trajectory calculator <NUM> for re-calculating the joint-space trajectory data <NUM> based on the adjusted target location for the selected marking. In this way, adjustments to the target location of the selected image may be made on the fly to further ensure that the selected marking is applied at a desired location. The GUI further may be enable the user to adjust the size of the selected marking or replace the selected marking with a different marking. In some examples, the same process of selecting a marking (new or overpainting) to a apply to a new target location, viewing a graphical representation of the selected marking and providing a user input to adjust the target location for such marking may be used in the field in addition or as an alternative to the predefined marking data.

In view of the foregoing structural and functional features described above, a method in accordance with various aspects of the present disclosure will be better appreciated with reference to <FIG>. While, for purposes of simplicity of explanation, the methods are shown and described as executing serially, such methods are not limited by the illustrated order. Some actions could occur in different orders and/or concurrently from that shown. Moreover, not all illustrated features may be required to implement a method. The method may be implemented by hardware (e.g., implemented in one or more computers, field programmable gate array (FPGA) and/or by discrete components), firmware and/or software (e.g., machine readable instructions stored in non-transitory media) or a combination of hardware and software.

<FIG> depicts an example method <NUM> for applying a selected marking to a target location. The method may be implemented utilizing any of the hardware and/or software disclosed herein with respect to <FIG> and <FIG>. The method <NUM> begins at <NUM> in which marking data is stored (e.g., in non-transitory machine-readable media). The marking data (e.g., data <NUM>, <NUM>, <NUM>) can specify a selected marking that is to be applied at a respective target location, such as disclosed herein. A target location can be specified as geospatial coordinates and orientation (e.g., marking codes) with respect to a reference frame of the selected marking.

At <NUM>, corresponding task plan data can also be stored (in memory). The task plan data can specify a process plan to create the selected mark with respect to a marking reference frame (part of the marking data stored at <NUM>) and various tool parameters. For example, the task plan data can be stored as a vector graphic to describe the path of a corresponding paint head to apply the selected marking in Cartesian (2D or 3D) space. The task plan is independent of the target location. As an example, a respective task plan may be associated with each available marking for a given application tool. If the application tool changes, the task plan may be adapted accordingly.

At <NUM>, a current pose of the application tool is determined. As disclosed herein, the pose of the application tool can be determined (e.g., by location calculator <NUM>, <NUM>) based on sensor data acquired from one or more sensors (e.g., <NUM>, <NUM>, <NUM>, <NUM>) having known positions with respect to the vehicle. At <NUM>, a determination is made whether the target location is within the zone of reachability for the application tool based on the pose at <NUM>. If the target location is within range, the method proceeds to <NUM> and a joint-space trajectory is computed. The joint-space trajectory can be computed (e. g, by marking control <NUM>, calculator <NUM>) based on the task plan data at <NUM> and the determined current pose of the application tool provided at <NUM>. The joint-space trajectory enables the application tool to apply the selected markings at the target location provided at <NUM>.

If the determination at <NUM> indicates that the target location is not within range (e.g., determined by reachability analyzer <NUM>) of the application tool for applying the selected marking or at least a substantial portion thereof, the method proceeds to <NUM> in which the vehicle can be moved or the target location adjusted. Based upon the vehicle movement and/or adjustment of target location, the method can return to <NUM>. This process can repeat until determining at <NUM> that the target location is within the zone of reachability of the application tool. After the joint-space trajectory has been computed at <NUM>, the method proceeds to <NUM> in which the application tool is controlled (e.g., by controller <NUM>, <NUM>) to apply the marking according to the joint-space trajectory associated with the determined pose of the application tool at <NUM>. After the marking has been applied at <NUM>, a next marking can be accessed at <NUM>, such as described in the marking data and loaded into memory for applying the next selected marking at its respective next target location. The vehicle may be moved at <NUM> and/or the target location changed at <NUM> such that the next marking resides within the zone of reachability for the tool. It is understood that the next marking may be identical or different and further may be adjusted based on a selection of the user.

<FIG> is a flow diagram depicting another example method <NUM> to control applying markings to a surface. At <NUM>, the method includes storing marking data (e.g., data <NUM>, <NUM>, <NUM>) to specify at least one marking that an application tool is to apply at a target location along an application path of travel for the vehicle. At <NUM>, geospatial coordinate data is received (e.g., a GPS device <NUM>, <NUM>) to represent a current pose of a vehicle along the application path of travel for the vehicle. At <NUM>, fiducials are sensed by at least one other sensor (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) along the application path of travel. The sensed data can be stored in one or more non-transitory machine-readable media. At <NUM>, fiducial data representing a fiducial coordinate frame for each of the sensed fiducials is determined from such sensor data (e.g., by sensor fusion function <NUM>, <NUM> or precision localization function) along the application path of travel with respect to a reference coordinate frame.

At <NUM>, a transformation is computed (e.g., by sensor fusion function <NUM>, transformation calculator <NUM>) to correlate the fiducial coordinate frame for each of the sensed fiducials along the application path of travel to a spatial coordinate frame for respective fiducials sensed along a previous survey path of travel. The application path of travel by the vehicle is to approximate the survey path of travel (e.g., by driving the vehicle along the same road). At <NUM>, a pose of the application tool is determined (e.g., by location calculator <NUM>, <NUM>, <NUM>) along the application path of travel based on the transformation and the geospatial coordinate data.

Claim 1:
A computer-implemented method comprising:
storing marking data (<NUM>, <NUM>, <NUM>) to specify at least one selected marking (<NUM>), the marking data (<NUM>, <NUM>, <NUM>) including a machine-readable description of the selected marking (<NUM>) and a marking reference coordinate frame (<NUM>) for the selected marking (<NUM>);
storing task plan data (<NUM>) describing a process executable by an application tool (<NUM>) to apply the selected marking (<NUM>) based on the marking data (<NUM>, <NUM>, <NUM>) and at least one parameter of the application tool (<NUM>);
determining a pose of the application tool (<NUM>); and
computing a joint-space trajectory of the application tool (<NUM>) based on the task plan data (<NUM>) and the pose of the application tool (<NUM>), the joint-space trajectory providing instructions to control the application tool (<NUM>) to apply the selected marking (<NUM>) at a target location,
characterized in that the marking (<NUM>) comprises at least one of a graphical object to be applied to a surface at the target location, removing the surface at the target location, or changing the surface at the target location.