Positioning method and apparatus, and vehicle

Abstract

A positioning method and apparatus, and a vehicle. The method can be applied to the field of intelligent driving. The method (300) comprises: acquiring first relative pose change information of a vehicle and an absolute pose thereof, wherein the first relative pose change information is obtained by means of time differences between GNSS signals received in different time units (S310); and on the basis of the first relative pose change information and the absolute pose, positioning the vehicle, so as to obtain a first positioning result (S320). The method can be applied to intelligent vehicles or electric vehicles, and does not rely on differential information and precise ephemeris during vehicle positioning, which is conducive to reducing the cost of using an autonomous driving function.

Description

Positioning method, apparatus and vehicle

[0001] The present application claims priority to the Chinese patent application No. 202411341838.7, filed on September 24, 2024, and entitled “Positioning method, apparatus and vehicle”, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of intelligent driving, and more particularly, to a positioning method, apparatus and vehicle. BACKGROUND

[0003] High-precision global navigation satellite system (GNSS) positioning is a key technology in autonomous driving. In the current mainstream scheme, real time kinematic (RTK) information or precise point positioning (PPP) is used to achieve high-precision GNSS positioning. However, the high-precision positioning scheme using RTK or PPP needs to subscribe to external services (such as differential information and precise ephemeris) at a high price, which greatly increases the cost of using autonomous driving functions. SUMMARY

[0004] The present application provides a positioning method, apparatus and vehicle, which do not rely on differential information and precise ephemeris in the process of positioning the vehicle, and helps to reduce the cost of using autonomous driving functions.

[0005] In a first aspect, the present application provides a positioning method, comprising: obtaining first relative pose change information of a vehicle and absolute pose, the first relative pose change information being obtained by time difference between GNSS signals received in different time units; and positioning the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

[0006] Based on the above technical solution, the process of positioning the vehicle does not rely on differential information and precise ephemeris, which helps to reduce the cost of using autonomous driving functions. At the same time, the relative pose change information obtained by time difference between GNSS signals received in different time units is used to position the vehicle, which helps to achieve high-precision positioning of the vehicle due to the high precision (e.g., up to centimeter level) of the relative pose change information.

[0007] The time difference between GNSS signals received in different time units can eliminate common errors, such as receiver clock error, multipath error, etc.

[0008] In some possible implementation manners, the GNSS signals received in different time units include GNSS signals received in two adjacent frames.

[0009] In some possible implementation manners, the first relative pose change information and the absolute pose are measured values, and the first positioning result includes another absolute pose of the vehicle.

[0010] With reference to the first aspect, in some possible implementation manners of the first aspect, the vehicle is positioned according to the first relative pose change information and the absolute pose to obtain a first positioning result, including: constructing a factor graph according to the first relative pose change information and the absolute pose; and performing optimization solving on the factor graph to obtain the first positioning result.

[0011] Based on the technical solution described above, the factor graph can be constructed based on the first relative pose change information and the absolute pose. The positioning result of the vehicle is obtained by performing optimization solving on the factor graph. In this way, the solving process does not depend on the differential information and the precise ephemeris, which helps to reduce the cost of using the automatic driving function.

[0012] With reference to the first aspect, in some possible implementation manners of the first aspect, the factor graph includes a plurality of nodes and a plurality of edges, the plurality of nodes include at least a solution node, a first node, a second node and an edge node, and the plurality of edges include at least a first edge, a second edge and a third edge, where the first node is configured to measure the absolute pose, the second node is configured to measure the first relative pose change information, the first edge is an edge between the edge node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

[0013] Based on the technical solution described above, the factor graph can be constructed by the plurality of nodes and the plurality of edges. The positioning result of the vehicle is obtained by performing optimization solving on the factor graph.

[0014] In some possible implementation manners, the first node includes one or more nodes configured to measure the absolute pose of the vehicle.

[0015] In some possible implementation manners, the second node includes one or more nodes configured to measure the relative pose change information of the vehicle, and the one or more nodes configured to measure the relative pose change information of the vehicle include a node configured to measure the first relative pose change information.

[0016] In some possible implementation manners, the factor graph can further include a third node and a fourth edge, the third node can represent a wheel speed meter, and the fourth edge can be an edge between the third node and the solution node.

[0017] In some possible implementation manners of the first aspect, the method further includes: obtaining data collected by a sensor outside the vehicle cabin; determining a feature element according to the data; and matching the feature element with an element on the map to obtain the absolute pose.

[0018] According to the technical solution described above, the first relative pose change information and the absolute pose are input into the prediction model to obtain the first positioning result. In this way, the positioning result of the vehicle is obtained by the prediction model, and the positioning result is obtained without relying on the difference information and the precise ephemeris, which helps to reduce the cost of using the automatic driving function.

[0019] In some possible implementation manners of the first aspect, the method further includes: obtaining data collected by a sensor outside the vehicle cabin; determining a feature element according to the data; and matching the feature element with an element on the map to obtain the absolute pose.

[0020] According to the technical solution described above, the feature element determined by using the data collected by the sensor outside the vehicle cabin is matched with the element on the map, which can eliminate the deviation between the GNSS solution result and the world coordinate system, and helps to improve the positioning accuracy of the vehicle.

[0021] In some possible implementation manners, the element on the map includes a road element (for example, a lane line, a turning sign, or the like) on the map.

[0022] In some possible implementation manners of the first aspect, the method further includes: determining a first absolute pose of the vehicle at a first time according to the first relative pose change information and the absolute pose; obtaining second relative pose change information of the vehicle between the first time and a second time, the second time being later than the first time; and determining a second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

[0023] In some possible implementation manners, the second absolute pose of the vehicle is determined according to the first absolute pose and the second relative pose change information, including: interpolating the first absolute pose according to the second relative pose change information to obtain the second absolute pose.

[0024] According to the technical solution described above, since there is a certain lag between the first absolute pose and the current system time, the first absolute pose is interpolated according to the second relative pose change information between the first time and the second time to obtain the second absolute pose of the vehicle. In this way, the positioning accuracy of the vehicle can be further improved.

[0025] With reference to the first aspect, in some implementations of the first aspect, the method further includes sending the first positioning result to a perception module.

[0026] With reference to the first aspect, in some implementations of the first aspect, the method further includes controlling the vehicle to travel according to the positioning result.

[0027] In a second aspect, the present application provides a positioning method, the method comprising: obtaining GNSS signals received in different time units; determining first relative pose change information according to time differences between the GNSS signals received in different time units.

[0028] With reference to the second aspect, in some implementations of the second aspect, the method further includes sending the first relative pose change information to a positioning module.

[0029] In a third aspect, a positioning apparatus is provided, the apparatus comprising: an obtaining unit configured to obtain first relative pose change information and an absolute pose of a vehicle, the first relative pose change information being obtained from time differences between GNSS signals received in different time units; and a positioning unit configured to position the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

[0030] With reference to the third aspect, in some implementations of the third aspect, the positioning unit is specifically configured to: construct a factor graph according to the first relative pose change information and the absolute pose; and optimize and solve the factor graph to obtain the first positioning result.

[0031] With reference to the third aspect, in some implementations of the third aspect, the factor graph comprises a plurality of nodes and a plurality of edges, the plurality of nodes comprising at least a solution node, a first node, a second node and a marginalized node, and the plurality of edges comprising at least a first edge, a second edge and a third edge, wherein the first node is configured to measure the absolute pose, the second node is configured to measure the first relative pose change information, the first edge is an edge between the marginalized node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

[0032] With reference to the third aspect, in some implementations of the third aspect, the positioning unit is specifically configured to: input the first relative pose information and the absolute pose information into a prediction model to obtain the first positioning result.

[0033] In some implementations of the third aspect, the apparatus further includes a first determining unit and an element matching unit, the obtaining unit is further configured to obtain data collected by a sensor outside the cabin, the first determining unit is configured to determine a feature element according to the data, and the element matching unit is configured to match the feature element with elements on the map to obtain the absolute pose.

[0034] In some implementations of the third aspect, the apparatus further includes a second determining unit configured to determine a first absolute pose of the vehicle at the first time according to the first relative pose change information and the absolute pose, the obtaining unit is configured to obtain second relative pose change information of the vehicle between the first time and a second time, the second time being later than the first time, and the positioning unit is specifically configured to determine a second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

[0035] In some implementations of the third aspect, the apparatus further includes a sending unit configured to send the first positioning result to a perception module.

[0036] In some implementations of the third aspect, the apparatus further includes a control unit configured to control the vehicle to travel according to the positioning result.

[0037] In a fourth aspect, the present application provides a positioning apparatus, which includes an obtaining unit configured to obtain GNSS signals received at different time units, and a determining unit configured to determine first relative pose change information according to time differences between the GNSS signals received at different time units.

[0038] In some implementations of the fourth aspect, the apparatus further includes a sending unit configured to send the first relative pose change information to a positioning module.

[0039] In a fifth aspect, the present application provides a positioning apparatus, which includes a memory configured to store a computer program and a processor configured to execute the computer program in the memory, so that the intelligent driving apparatus can implement the method in any possible implementation manner of the first aspect or the second aspect.

[0040] In a sixth aspect, the present application provides a positioning system, which includes a perception system and a computing platform, the computing platform including the apparatus of the third aspect, the fourth aspect or the fifth aspect.

[0041] In a seventh aspect, the present application provides a vehicle including the apparatus of the third aspect, the fourth aspect or the fifth aspect, or including the system of the sixth aspect.

[0042] The vehicle in the present application is a vehicle in a broad sense, which can be a traffic tool (such as a commercial vehicle, a passenger vehicle, a motorcycle, a flying vehicle, a train, etc.), an industrial vehicle (such as a forklift, a trailer, a tractor, etc.), an engineering vehicle (such as an excavator, a bulldozer, a crane, etc.), an agricultural device (such as a mower, a harvester, etc.), a recreational device, a toy vehicle, etc. The type of the vehicle is not limited in the embodiments of the present application.

[0043] In an eighth aspect, the present application provides a computer program product, which comprises computer program codes, when the computer program codes are run on a computer, the computer program codes make the computer execute the method in any possible implementation manner of the first aspect or the second aspect.

[0044] In a ninth aspect, the present application provides a computer readable storage medium, which stores a computer program, when the computer program is run on a computer, the computer program makes the computer execute the method in any possible implementation manner of the first aspect or the second aspect.

[0045] In a tenth aspect, the present application provides a chip, which comprises a circuit for executing the method in any possible implementation manner of the first aspect or the second aspect. BRIEF DESCRIPTION OF DRAWINGS

[0046] FIG. 1 is a functional block diagram of a vehicle according to an embodiment of the present application.

[0047] FIG. 2 is a schematic block diagram of an intelligent driving system according to an embodiment of the present application.

[0048] FIG. 3 is a schematic flowchart of a positioning method according to an embodiment of the present application.

[0049] FIG. 4 is a schematic diagram of a positioning system according to an embodiment of the present application.

[0050] FIG. 5 is a factor graph constructed by relative pose change information and absolute pose according to an embodiment of the present application.

[0051] FIG. 6 is a schematic flowchart of a positioning system according to an embodiment of the present application.

[0052] FIG. 7 shows possible implementation processes of GNSS_ODOM and GNSS_SPP according to an embodiment of the present application.

[0053] FIG. 8 is a schematic block diagram of a positioning device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B; in this document, "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist together, and B exists alone. "At least one" means one or more. For example, "at least one of A and B" is similar to "A and / or B", which describes the association relationship of the associated objects, which means that there can be three relationships, for example, at least one of A and B, which can represent: A exists alone, A and B exist together, and B exists alone.

[0055] In the embodiments of the present application, the prefix words such as "first", "second" are only used to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of ordinal words such as "first" and "second" in the embodiments of the present application does not limit the described objects, and the description of the described objects should be referred to the description of the context in the claims or embodiments, and should not be considered as redundant limitation because of the use of such prefix words. In addition, in the description of the embodiments, unless otherwise specified, the meaning of "multiple" is two or more.

[0056] FIG. 1 is a functional block diagram of a vehicle 100 provided by the embodiments of the present application. The vehicle 100 can include a perception system 110, a computing platform 120 and a display device 130, wherein the perception system 110 can include one or more sensors that sense information about the environment around the vehicle 100. For example, the perception system 110 can include a positioning system, which can be a global positioning system (GPS), a Beidou system or other positioning system. For another example, the perception system 110 can include one or more of an inertial measurement unit (IMU), an acceleration sensor, a laser radar, a millimeter wave radar, an ultrasonic radar and a camera.

[0057] Some or all functions of the vehicle 100 can be controlled by the computing platform 120. The computing platform 120 can include one or more processors, such as processors 121 through 12n (n is a positive integer), which are circuits having a processing capability of signals. In one implementation, the processors can be circuits having an instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a kind of microprocessor), a digital signal processor (DSP), or the like. In another implementation, the processors can be circuits having a certain function implemented by a logic relationship of hardware circuits, which is fixed or reconfigurable. For example, the processors can be hardware circuits implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration. It can be understood that the processor loads instructions to implement the functions of the above units. In addition, the processors can also be hardware circuits designed for artificial intelligence, which can be understood as a kind of ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), or the like. In addition, the computing platform 120 can also include a memory for storing instructions, and some or all of the processors 121 through 12n can call the instructions in the memory to implement corresponding functions.

[0058] The display device 130 in the cabin is mainly divided into two categories, the first category is a vehicle display screen, and the second category is a projection display screen, such as a head up display (HUD). The vehicle display screen is a physical display screen and is an important component of the in-vehicle infotainment system. Multiple display screens can be provided in the cabin, such as a digital instrument display screen, a center control screen, a display screen in front of a passenger (also referred to as a front passenger) at a co-driver position, a display screen in front of a left rear passenger, and a display screen in front of a right rear passenger, or even a vehicle window can be used as a display screen for display. The head up display, also known as a head-up display system, is mainly used for displaying driving information such as speed, navigation, etc. on a display device (such as a windshield) in front of the driver. This reduces the time for the driver to change his / her line of sight and avoids changes in the pupil caused by the driver's line of sight changing, thereby improving driving safety and comfort. The HUD includes, for example, a combiner-HUD (C-HUD) system, a windshield-HUD (W-HUD) system, and an augmented reality HUD (AR-HUD). It should be understood that other types of systems can also appear as the technology evolves, and the present application does not limit this.

[0059] The display device 130 described above is illustrated by taking the vehicle display screen and the projection display screen as examples, and embodiments of the present application are not limited thereto. For example, the display device 130 can also be a light display screen or a projection screen.

[0060] Optionally, the structure of the vehicle 100 described above is only schematic, and in actual applications, various components in the vehicle 100 described above can be added or deleted according to actual needs.

[0061] The vehicle 100 can include an intelligent driving system, which can include an advanced driving assistant system (ADAS) and an autonomous driving system (ADS). The intelligent driving system uses various sensors (including but not limited to laser radar, millimeter wave radar, camera, ultrasonic sensor, global positioning system, and inertial measurement unit) on the vehicle to obtain information from the surroundings of the vehicle, and analyzes and processes the obtained information to realize functions such as obstacle perception, target recognition, vehicle positioning, path planning, driver monitoring / reminding, etc., thereby improving the safety, automation level, and comfort of vehicle driving.

[0062] For example, FIG. 2 shows a schematic block diagram of an intelligent driving system according to an embodiment of the present application. The intelligent driving system can include three functional modules: a perception module 210, a planning module 220, and a control module 230. The perception module 210 perceives the environment around the vehicle body through sensors and outputs corresponding perception data to the planning module 220. The planning module 220 obtains road topology and target object information based on the information obtained by the perception module 210. The planning module 220 can determine a planning trajectory for a period of time based on the road topology and target object information. The planning module 220 can send the planning trajectory to the control module 230. The control module 230 can output a control signal after receiving the planning trajectory from the planning module 220, and can control the actuators to take corresponding actions, such as steering, accelerating, decelerating, and the like.

[0063] In an embodiment of the present application, the perception module 210 can receive the positioning information of the vehicle 100 sent by the fusion localization module.

[0064] The above fusion localization module is used to output a high-precision positioning result of the vehicle 100 to the perception module 210. The fusion localization module 240 can be located in the intelligent driving system described above, or can also be located outside the intelligent driving system. The present application does not make specific limitations on this.

[0065] The above perception module 210 can be the perception system 110 described above, and the planning module 220 and the control module 230 can be located in the computing platform 120 described above.

[0066] The degree to which a vehicle-based driving automation system can perform dynamic driving tasks is divided into levels 0-5 (or L0-L5) based on the role allocation in performing dynamic driving tasks and the presence or absence of operational design domain (ODD) limits, such as external conditions suitable for the functional operation of the driving automation system as determined when the system is designed, such as roads, traffic, weather, lighting, and the like). Among the six levels of driving automation, levels 0-2 are driving assistance, and the system assists humans in performing dynamic driving tasks, and the driving subject is still the driver; levels 3-5 are automatic driving, and the system replaces humans to perform dynamic driving tasks under the designed operating conditions, and when the function is activated, the driving subject is the system. The names and definitions of each level are as follows:

[0067] A level 0 driving automation (may also be referred to as emergency assistance) system is not capable of continuously performing vehicle lateral or longitudinal motion control in dynamic driving tasks, but has the capability to perform some target and event detection and response in dynamic driving tasks. A level 1 driving automation (may also be referred to as partial driver assistance) system continuously performs vehicle lateral or longitudinal motion control in dynamic driving tasks under its design operating conditions, and has the capability to perform some target and event detection and response appropriate to the vehicle lateral or longitudinal motion control performed. A level 2 driving automation (may also be referred to as combined driver assistance) system continuously performs vehicle lateral and longitudinal motion control in dynamic driving tasks under its design operating conditions, and has the capability to perform some target and event detection and response appropriate to the vehicle lateral and longitudinal motion control performed. A level 3 driving automation (may also be referred to as conditionally automated driving) system continuously performs all dynamic driving tasks under its design operating conditions. A level 4 driving automation (may also be referred to as highly automated driving) system continuously performs all dynamic driving tasks under its design operating conditions and a minimal risk criterion. A level 5 driving automation (may also be referred to as fully automated driving) system continuously performs all dynamic driving tasks under all foreseeable conditions and a minimal risk criterion. Generally, intelligent driving systems are generally L2-L5, such as ADAS is L2, and ADS is L3-L5.

[0068] FIG. 3 shows a schematic flowchart of a positioning method 300 provided by an embodiment of the present application. The method 300 can be performed by the vehicle 100 described above; or the method 300 can be performed by the computing platform 120 described above; or the method 300 can be performed by a processor, a chip or a circuit in the computing platform 120 described above; or the method 300 can be performed by the intelligent driving system described above; or the method 300 can be performed by the fusion positioning module 240 described above. The method 300 comprises:

[0069] S310, obtaining a first relative pose change information and an absolute pose of the vehicle, the first relative pose change information being obtained from a time difference between GNSS signals received in different time units.

[0070] For example, the GNSS signals received in different time units can be GNSS signals received by the vehicle in two adjacent frames, or GNSS signals received in a first frame and a second frame that are separated by a preset number of frames (for example, separated by 1 frame).

[0071] The GNSS signals received in different time units can also be understood as GNSS signals at different time points.

[0072] FIG. 4 shows a schematic diagram of a positioning system 400 provided by an embodiment of the present application. The positioning system 400 includes a fusion positioning module 240, a relative positioning module (Relative) 410, a GNSS odometer (GNSS_ODOM) 420, a GNSS single point positioning (GNSS_SPP) 430, and a vector localization module (vector localization) 440. The fusion positioning module 240 can receive positioning information output by the relative positioning module 410, the GNSS odometer 420, the GNSS single point positioning 430, and the vector localization module 440, and output a high-precision positioning result of the vehicle based on the positioning information provided by these modules and send the result to the perception module 210.

[0073] For example, the positioning information output by the relative positioning module 410 includes relative pose change information of the vehicle. In some scenarios where GNSS signals are lost (for example, when the vehicle is in a tunnel or an underground garage), the vehicle can be positioned in combination with the relative pose change information output by the relative positioning module 410. The relative positioning module 410 outputs the relative pose change information at a high frequency. Since the relative positioning module 410 relies on inertial sensors (for example, a wheel speed sensor or an IMU), the relative pose change information output by the relative positioning module 410 can have a continuous drift, thereby causing cumulative errors.

[0074] For example, the positioning information output by the GNSS odometer 420 includes relative pose change information of the vehicle, which is obtained from a time difference between GNSS signals received by the vehicle in different time units (for example, in two adjacent frames). The GNSS odometer outputs the relative pose change information at a low frequency, but the accuracy is higher, which can reach centimeter level. For example, in a scenario where the vehicle is in a good GNSS signal environment, the fusion positioning module 240 can perform high-precision positioning of the vehicle in combination with the relative pose change information output by the GNSS odometer.

[0075] For example, the positioning information output by the GNSS single-point solution 430 includes information of an absolute pose of the vehicle. The GNSS single-point solution 430 determines the absolute pose of the vehicle by receiving signals of multiple satellites, calculating pseudo-distances, and establishing observation equations, and iteratively solving three-dimensional coordinates and clock bias of the receiver by using a least square method.

[0076] For example, the positioning information output by the vector positioning module 440 includes information of an absolute pose of the vehicle. For example, a simultaneous localization and mapping (SLAM) module can receive images captured by a camera and reconstruct feature points based on the images to obtain reconstructed elements. The vector positioning module 440 can receive information of the reconstructed elements sent by the SLAM module, and match the information of the reconstructed elements and elements on a map to obtain the absolute pose of the vehicle.

[0077] Optionally, the first relative pose information in the above step S310 includes relative pose information output by the GNSS odometry 420, or the first relative pose information in the above step S310 includes relative pose information output by the relative positioning module 410 and the GNSS odometry 420.

[0078] Optionally, the absolute pose in the above step S310 includes absolute pose information output by the GNSS single-point solution 430 and / or the vector positioning module 440.

[0079] S320, positioning the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

[0080] Optionally, before positioning the vehicle according to the first relative pose change information and the absolute pose, the method 300 further includes aligning the first relative pose change information and the absolute pose according to timestamps.

[0081] For example, the fusion positioning module 240 can receive and buffer positioning information output by the relative positioning module 410, the GNSS odometry 420, the GNSS single-point solution 430, and the vector positioning module 440, and align the positioning information according to timestamps.

[0082] Optionally, positioning the vehicle according to the first relative pose change information and the absolute pose includes: constructing a factor graph according to the first relative pose information and the absolute pose information; and optimizing and solving the factor graph to obtain the first positioning result.

[0083] Optionally, the factor graph includes a plurality of nodes and a plurality of edges, the plurality of nodes including at least a solution node, a first node, a second node, and a marginalized node, and the plurality of edges including at least a first edge, a second edge, and a third edge, wherein the first node is used to measure the absolute pose, the second node is used to measure the first relative pose change information, the first edge is an edge between the marginalized node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

[0084] For example, the GNSS positioning result after optimization can be obtained by constructing a factor graph based on the aligned information, defining a loss function of the real position of the vehicle and each observation source, and iteratively optimizing the real position of the vehicle. The observation source can be the relative positioning module 410, the GNSS odometer 420, the GNSS single-point solution 430, and the vector positioning module 440.

[0085] For example, FIG. 5 shows a factor graph constructed based on the relative pose change information and the absolute pose according to an embodiment of the present application.

[0086] For example, the factor graph includes a plurality of nodes and a plurality of edges, the plurality of nodes including S nodes (representing solution nodes, i.e., the real pose of the vehicle), M nodes (representing marginalized nodes), G nodes (representing measurements of the GNSS single-point solution 430), V nodes (representing measurements of the vector positioning module 440), R nodes (representing measurements of the relative positioning module 410), and O nodes (representing measurements of the GNSS odometer 420).

[0087] For example, the plurality of edges includes an edge between the M node and the S node (representing a constraint caused by the marginalization algorithm), an edge between the G node and the S node (representing a constraint of the GNSS single-point solution 430 on the solution node at the same time), an edge between the S node and the R node (representing a constraint of the relative positioning module 410 on the solution nodes of two adjacent frames), an edge between the S node and the O node (representing a constraint of the GNSS odometer 420 on the solution nodes of two adjacent frames), and an edge between the V node and the S node (representing a constraint of the vector positioning module 440 on the solution node at the same time).

[0088] The above marginalized node can be obtained by a variety of marginalization algorithms. The marginalized node is constructed by retaining historical solution information, and forms a constraint on the first frame solution node in the graph optimization solution process. The marginalized node is updated after each graph optimization solution to maintain the constraint of the historical frames on the current graph.

[0089] Exemplarily, the above edge node can be the M node, the first node can be the G node and the V node, the second node can be the R node and the O node, and the solution node can be the S node. The first edge can be the edge between the M node and the S node, the second edge can be the edge between the G node and the S node and the edge between the V node and the S node, and the third edge can be the edge between the R node and the S node and the edge between the O node and the S node.

[0090] The factor graph shown in FIG. 5 is merely illustrative, and embodiments of the present application are not limited thereto. Exemplarily, more nodes can be included in the factor graph. For example, a node representing a wheel speed meter can also be included. Exemplarily, fewer nodes can be included in the factor graph. For example, the V node can be included and the G node can not be included, or the G node can be included and the V node can not be included.

[0091] Exemplarily, the nodes in FIG. 5 can include the M node, the S node, the G node and the O node. The above edge node can be the M node, the first node can be the G node, the second node can be the O node, and the solution node can be the S node. The first edge can be the edge between the M node and the S node, the second edge can be the edge between the G node and the S node, and the third edge can be the edge between the O node and the S node.

[0092] Exemplarily, the nodes in FIG. 5 can include the M node, the S node, the V node and the O node. The above edge node can be the M node, the first node can be the V node, the second node can be the O node, and the solution node can be the S node. The first edge can be the edge between the M node and the S node, the second edge can be the edge between the V node and the S node, and the third edge can be the edge between the O node and the S node.

[0093] Exemplarily, the nodes in FIG. 5 can include the M node, the S node, the G node, the V node and the O node. The above edge node can be the M node, the first node can be the G node and the V node, the second node can be the O node, and the solution node can be the S node. The first edge can be the edge between the M node and the S node, the second edge can be the edge between the G node and the S node and the edge between the V node and the S node, and the third edge can be the edge between the O node and the S node.

[0094] Optionally, the vehicle is positioned according to the first relative pose change information and the absolute pose, including: inputting the first relative pose change information and the absolute pose into a prediction model to obtain the first positioning result.

[0095] Exemplarily, the relative pose change information output by the GNSS odometry 420 and the absolute pose output by the GNSS point solution 430 can be input into the prediction model, so as to obtain the first positioning result of the vehicle.

[0096] Exemplarily, the prediction model can be trained by a training data set. For example, the training data set can include sample relative pose change information and sample absolute pose, and labeled vehicle pose information. The prediction model can be trained by the training data set, so as to be generalized to various scenarios and predict the vehicle pose information.

[0097] The above prediction model can be a neural network (NN), for example, a transformer, a multi-layer perceptron (MLP), a residual neural network (ResNet), or a recurrent neural network (RNN); or can also be other machine learning algorithms, for example, a support vector machine (SVM) or a decision tree, etc.

[0098] Optionally, the vehicle is positioned according to the first relative pose change information and the absolute pose to obtain the first positioning result, including: determining the first absolute pose of the vehicle at the first time according to the first relative pose change information and the absolute pose; obtaining second relative pose change information of the vehicle between the first time and the second time, the second time being later than the first time; and determining the second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

[0099] Optionally, the second absolute pose of the vehicle is determined according to the first absolute pose and the second relative pose change information, including: interpolating the first absolute pose by the second relative pose change information to obtain the second absolute pose.

[0100] Exemplarily, the first absolute pose of the vehicle at the first time can be obtained by the graph optimization. The vehicle can obtain the second relative pose change information between the first time and the second time, the second time being later than the first time. The second absolute pose of the vehicle can be obtained by interpolating the first absolute pose by the second relative pose change information.

[0101] Exemplarily, the second relative pose change information includes the relative pose change information output by the relative positioning module 410 and / or the GNSS odometry 420 between the first time and the second time.

[0102] The embodiment of the present application further provides a positioning method, which can be executed by the vehicle 100, or the method can be executed by the computing platform 120, or the method can be executed by the processor, chip or circuit in the computing platform 120, or the method can be executed by the intelligent driving system, or the method can be executed by the GNSS odometer 420. The method comprises the following steps: acquiring GNSS signals received in different time units; determining first relative pose change information according to time differences between the GNSS signals received in different time units.

[0103] Optionally, the method further comprises: sending the first relative pose change information to a positioning module.

[0104] FIG. 6 shows a schematic flowchart of a positioning system 600 provided by the embodiment of the present application. The positioning system 600 comprises the GNSS_ODOM 420 and the GNSS_SPP 430, and GNSS raw data are input into the GNSS_ODOM 420 and the GNSS_SPP 430 respectively. Based on the input data collected by the IMU, the data collected by the wheel speed sensor and the vehicle dead reckoning (VDR) algorithm, the predicted position and speed of the vehicle can be obtained. The predicted position and speed obtained by the VDR algorithm can be input into the GNSS_ODOM 420 and the GNSS_SPP 430 respectively. The GNSS_SPP 430 can determine the absolute position and absolute position accuracy of the vehicle, and the speed and speed accuracy of the vehicle based on the GNSS raw data, the predicted position and speed. The GNSS_SPP 430 can further output the determined absolute position of the vehicle to the GNSS_ODOM 420. The GNSS_ODOM 420 can determine the relative position change information and position accuracy of the vehicle based on the GNSS raw data, the predicted position, the speed and the absolute position of the vehicle.

[0105] Dead reckoning (DR) algorithm is a navigation and positioning technology. Under the condition of knowing the current position, the next position can be calculated by measuring the moving distance and direction. Continuous autonomous positioning can be achieved. When the DR algorithm is used for a vehicle, it can be referred to as VDR algorithm.

[0106] Optionally, the information output by the GNSS_SPP 430 can also be fed back to the VDR.

[0107] FIG. 7 shows a possible implementation process of the GNSS_ODOM 420 and the GNSS_SPP 430 provided by the embodiment of the present application.

[0108] For example, the GNSS_ODOM 420 can output the relative position change information and the position accuracy of the vehicle based on the input information through key frame selection, data preprocessing, time update, measurement update, ambiguity fixing and quality control (QC).

[0109] The data processing procedure in the GNSS_ODOM 420 can be understood as a time difference procedure of the GNSS signals received by the GNSS_ODOM 420 in different time units (for example, adjacent two frames).

[0110] For example, the GNSS_SPP 430 can output the absolute position, the absolute position accuracy, the speed and the speed accuracy of the vehicle based on the input information through data preprocessing, multipath error identification, observation value noise reduction, time update, measurement update and QC.

[0111] Optionally, the time update in the GNSS_ODOM 420 and the GNSS_SPP 430 can be realized through the VDR assistance.

[0112] In the embodiments of the present application, the centimeter-level relative position result is calculated through the geometric constraint between epochs by using the millimeter-level GNSS carrier phase observation. Through the semi-tight combination scheme, the priori constraint on the position and the speed provided by the VDR can be utilized to realize the star selection and the weight adjustment of the GNSS, which is helpful to improve the positioning accuracy and the reliability of the vehicle.

[0113] FIG. 8 shows a schematic block diagram of a positioning apparatus 800 provided by the embodiments of the present application. The apparatus 800 comprises: an acquisition unit 810, configured to acquire first relative pose change information of a vehicle and an absolute pose, the first relative pose change information being obtained through time difference between GNSS signals received in different time units; and a positioning unit 820, configured to position the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

[0114] Optionally, the positioning unit 820 is specifically configured to: construct a factor graph according to the first relative pose change information and the absolute pose; and optimize and solve the factor graph to obtain the first positioning result.

[0115] Optionally, the factor graph comprises a plurality of nodes and a plurality of edges, the plurality of nodes at least comprising a solution node, a first node, a second node and an edge node, and the plurality of edges at least comprising a first edge, a second edge and a third edge, wherein the first node is configured to measure the absolute pose, the second node is configured to measure the first relative pose change information, the first edge is an edge between the edge node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

[0116] Optionally, the positioning unit 820 is specifically configured to: input the first relative pose information and the absolute pose information into a prediction model to obtain the first positioning result.

[0117] Optionally, the apparatus 800 further comprises a first determination unit and an element matching unit. The acquisition unit 810 is further configured to acquire data collected by a sensor outside the cabin. The first determination unit is configured to determine a feature element according to the data. The element matching unit is configured to match the feature element with an element on a map to obtain the absolute pose.

[0118] Optionally, the apparatus 800 further comprises a second determination unit. The second determination unit is configured to determine a first absolute pose of the vehicle at a first time according to the first relative pose change information and the absolute pose. The acquisition unit 810 is configured to acquire second relative pose change information of the vehicle between the first time and a second time, the second time being later than the first time. The positioning unit 820 is specifically configured to determine a second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

[0119] Optionally, the apparatus 800 further comprises a sending unit configured to send the first positioning result to a perception module. For example, the positioning apparatus 800 can be located in the fusion positioning module 240. After the positioning apparatus 800 obtains the first positioning result, the first positioning result can be sent to the perception module 210.

[0120] Optionally, the apparatus 800 further comprises a control unit configured to control the vehicle to travel according to the positioning result. For example, the positioning apparatus 800 can integrate the functions implemented by the fusion positioning module 240, the perception module 210, the planning module 220 and the control module 230.

[0121] For example, the acquisition unit 810 can be a computing platform or a processing circuit, a processor or a controller in the computing platform in FIG. 1. Taking the acquisition unit 810 as the processor 121 in the computing platform as an example, the processor 121 can acquire the first relative pose change information and the absolute pose of the vehicle.

[0122] For another example, the positioning unit 820 can be a computing platform or a processing circuit, a processor or a controller in the computing platform in FIG. 1. Taking the positioning unit 820 as the processor 122 in the computing platform as an example, the processor 122 can position the vehicle according to the first relative pose change information and the absolute pose acquired by the processor 121 to obtain the positioning result.

[0123] The functions implemented by the acquisition unit 810 and the functions implemented by the positioning unit 820 can be implemented by different processors, or can be implemented by the same processor, and the embodiments of the present application do not limit this.

[0124] The embodiments of the present application further provide a positioning device, which comprises: an acquisition unit, configured to acquire GNSS signals received in different time units; and a determination unit, configured to determine first relative pose change information according to time differences between the GNSS signals received in different time units.

[0125] Optionally, the device further comprises a sending unit, configured to send the first relative pose change information to a positioning module.

[0126] It should be understood that the division of the units in the above device is only a logical functional division, and all or part of the units can be integrated into one physical entity, or can be physically separated. In addition, the units in the device can be implemented in the form of processor calling software; for example, the device comprises a processor, the processor is connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any one of the above methods or to implement the functions of the units of the device, wherein the processor is, for example, a general processor, such as a CPU or a microprocessor, and the memory is an internal memory of the device or an external memory of the device. Alternatively, the units in the device can be implemented in the form of hardware circuit, and the functions of part or all of the units can be implemented by designing the hardware circuit, which can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of part or all of the units are implemented by designing the logical relationship of elements in the circuit; for example, in another implementation, the hardware circuit is a PLD, and taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to implement the functions of part or all of the units. All the units of the above device can be implemented in the form of processor calling software, or all the units can be implemented in the form of hardware circuit, or part of the units can be implemented in the form of processor calling software, and the remaining part can be implemented in the form of hardware circuit.

[0127] In the embodiments of the present application, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can implement certain functions through a logic relationship of a hardware circuit, which is fixed or can be reconfigured. For example, the processor is an ASIC or a PLD implemented hardware circuit, such as an FPGA. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration. It can be understood that the processor loads instructions to implement the functions of the above units.

[0128] It can be seen that each unit in the above apparatus can be one or more processors (or processing circuits) configured to implement the above methods, such as a CPU, a GPU, a NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms.

[0129] In addition, each unit in the above apparatus can be integrated together or can be independently implemented. In one implementation, these units are integrated together to implement a SoC. The SoC can include at least one processor for implementing any of the above methods or the functions of the units of the apparatus. The at least one processor can be different, such as including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0130] Embodiments of the present application also provide a positioning apparatus, which includes a processing unit and a storage unit, wherein the storage unit is configured to store instructions, and the processing unit is configured to execute the instructions stored in the storage unit, so that the apparatus executes the methods or steps executed by the above embodiments.

[0131] Optionally, if the positioning apparatus is located in an intelligent driving device, the above processing unit can be one or more of the processors 121-12n shown in FIG. 1.

[0132] Embodiments of the present application also provide a positioning system, which includes a perception system and a computing platform, and the computing platform includes the above positioning apparatus.

[0133] Embodiments of the present application also provide an intelligent driving device, which can include the above positioning apparatus or the above positioning system 600.

[0134] For example, the intelligent driving device can be the above vehicle 100.

[0135] The embodiment of the present application further provides a computer program product, which comprises computer program codes, and when the computer program codes run on a computer, the computer program codes make the computer execute the method in the above embodiment.

[0136] The embodiment of the present application further provides a computer readable medium, which stores program codes, and when the program codes run on a computer, the program codes make the computer execute the method in the above embodiment.

[0137] The embodiment of the present application further provides a chip, which comprises a circuit for executing the method in the above embodiment.

[0138] In the implementation process, each step of the above method can be completed by integrated logic circuits of hardware in the processor or instructions in the form of software. The method disclosed in the embodiment of the present application can be directly embodied as hardware processor execution completion or combined execution completion by hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable read-only memory, register or the like. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0139] It should be understood that the memory in the embodiment of the present application can include read-only memory and random access memory, and provide instructions and data to the processor.

[0140] It should also be understood that in various embodiments of the present application, the size of the serial number of each process described above does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software mode depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0143] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0144] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0145] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically separate unit, or two or more units can be integrated into one unit.

[0146] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0147] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A positioning method, characterized by, The method comprises: obtaining first relative pose change information and absolute pose of a vehicle, the first relative pose change information being obtained by time difference between GNSS signals received at different time units; positioning the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

2. The method of claim 1, wherein, The positioning of the vehicle according to the first relative pose change information and the absolute pose to obtain the first positioning result comprises: constructing a factor graph according to the first relative pose change information and the absolute pose; optimizing and solving the factor graph to obtain the first positioning result.

3. The method of claim 2, wherein, The factor graph comprises a plurality of nodes and a plurality of edges, the plurality of nodes comprising at least a solution node, a first node, a second node and a marginalized node, and the plurality of edges comprising at least a first edge, a second edge and a third edge, wherein the first node is used to measure the absolute pose, the second node is used to measure the first relative pose change information, the first edge is an edge between the marginalized node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

4. The method of claim 1, wherein, The positioning of the vehicle according to the first relative pose change information and the absolute pose to obtain the first positioning result comprises: inputting the first relative pose information and the absolute pose information into a prediction model to obtain the first positioning result.

5. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: obtaining data collected by a sensor outside a cabin; determining a feature element according to the data; matching the feature element with an element on a map to obtain the absolute pose.

6. The method according to any one of claims 1 to 5, characterized in that, The positioning of the vehicle according to the first relative pose change information and the absolute pose to obtain the first positioning result comprises: determining a first absolute pose of the vehicle at a first time according to the first relative pose change information and the absolute pose; obtaining second relative pose change information of the vehicle between the first time and a second time, the second time being later than the first time; determining a second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: sending the first positioning result to a perception module.

8. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: controlling the vehicle to travel according to the positioning result.

9. A positioning method characterized by, The method comprises: obtaining GNSS signals received at different time units; determining first relative pose change information according to time difference between the GNSS signals received at different time units.

10. The method of claim 9, wherein, The method further comprises: sending the first relative pose change information to a positioning module.

11. A positioning device, characterized by The method comprises: an obtaining unit, configured to obtain first relative pose change information and absolute pose of a vehicle, the first relative pose change information being obtained by time difference between GNSS signals received at different time units; a positioning unit, configured to position the vehicle according to the first relative pose change information and the absolute pose to obtain a first positioning result.

12. The apparatus of claim 11, wherein, The positioning unit is specifically configured to: construct a factor graph according to the first relative pose change information and the absolute pose; Optimizing and solving the factor graph to obtain the first positioning result.

13. The apparatus of claim 12, wherein, The factor graph includes a plurality of nodes and a plurality of edges, the plurality of nodes at least including a solution node, a first node, a second node and a marginalized node, and the plurality of edges at least including a first edge, a second edge and a third edge, The first node is configured to measure the absolute pose, the second node is configured to measure the first relative pose change information, the first edge is an edge between the marginalized node and the solution node, the second edge is an edge between the first node and the solution node, and the third edge is an edge between the second node and the solution node.

14. The apparatus of claim 11, wherein, The positioning unit is specifically configured to: input the first relative pose information and the absolute pose information into a prediction model to obtain the first positioning result.

15. The apparatus of any one of claims 11 to 14, wherein, The device further includes a first determination unit and an element matching unit, The acquisition unit is further configured to acquire data collected by a sensor outside the cabin; The first determination unit is configured to determine a feature element according to the data; The element matching unit is configured to match the feature element with an element on a map to obtain the absolute pose.

16. The apparatus of any one of claims 11 to 15, wherein, The device further includes a second determination unit, The second determination unit is configured to determine a first absolute pose of the vehicle at a first time according to the first relative pose change information and the absolute pose; The acquisition unit is configured to acquire second relative pose change information of the vehicle between the first time and a second time, the second time being later than the first time; The positioning unit is specifically configured to determine a second absolute pose of the vehicle according to the first absolute pose and the second relative pose change information.

17. The apparatus of any one of claims 11 to 16, wherein, The device further includes: A sending unit configured to send the first positioning result to a perception module.

18. The apparatus of any one of claims 11 to 16, wherein, The device further includes: A control unit configured to control vehicle driving according to the positioning result.

19. A positioning device, characterized by The device includes: An acquisition unit configured to acquire GNSS signals received at different time units; A determination unit configured to determine first relative pose change information according to time differences between the GNSS signals received at the different time units.

20. The apparatus of claim 19, wherein, The device further includes: A sending unit configured to send the first relative pose change information to a positioning module.

21. A positioning device, characterized by The device includes: A processor configured to execute a computer program stored in a memory to enable the device to perform the method of any one of claims 1 to 10.

22. The apparatus of claim 21, wherein, The device further includes the memory.

23. A positioning system, characterized by The positioning system includes a perception system and a computing platform, and the computing platform includes the device of any one of claims 11 to 22.

24. A vehicle characterized by comprising: The device of any one of claims 11 to 22, or the system of claim 23.

25. A computer readable storage medium, characterized in that, Instructions stored thereon, which, when executed by a processor, enable the processor to implement the method of any one of claims 1 to 10.

26. A computer program product, characterised in that, The computer program product includes computer program code, which, when executed on a computer, enables the computer to implement the method of any one of claims 1 to 10.

27. A chip, characterized by The chip comprises circuitry for performing the method of any of claims 1 to 10.

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