CN111077555A - Positioning method and device - Google Patents

Positioning method and device Download PDF

Info

Publication number
CN111077555A
CN111077555A CN202010210131.8A CN202010210131A CN111077555A CN 111077555 A CN111077555 A CN 111077555A CN 202010210131 A CN202010210131 A CN 202010210131A CN 111077555 A CN111077555 A CN 111077555A
Authority
CN
China
Prior art keywords
satellite
vehicle
positioning
information
gnss
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010210131.8A
Other languages
Chinese (zh)
Other versions
CN111077555B (en
Inventor
郝立良
申浩
魏子谦
周小红
高春乐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Sankuai Online Technology Co Ltd
Original Assignee
Beijing Sankuai Online Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Sankuai Online Technology Co Ltd filed Critical Beijing Sankuai Online Technology Co Ltd
Priority to CN202010210131.8A priority Critical patent/CN111077555B/en
Publication of CN111077555A publication Critical patent/CN111077555A/en
Application granted granted Critical
Publication of CN111077555B publication Critical patent/CN111077555B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
    • G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)

Abstract

The specification discloses a positioning method and a positioning device. According to the scheme, satellite data sent by GNSS are obtained, position information of each satellite is determined, and pseudo range information between each satellite and a vehicle is determined according to time difference of sending and receiving the satellite data. And determining the satellite positioning error positioned by the GNSS according to the position information of each satellite, the predicted positioning information of the vehicle at the current moment and the pseudo-range information. And determining a sensing positioning error according to the sensing data acquired by each sensor and the predicted positioning information of the vehicle at the current moment. And (3) adjusting the predicted positioning information by taking the minimization of the sum of the satellite and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment. According to the scheme, the GNSS and the sensors are matched with each other for positioning, the error equation is established by fully utilizing the GNSS and the original data in the sensors, and the positioning precision of the vehicle at the current moment is improved by adopting an optimization algorithm.

Description

Positioning method and device
Technical Field
The specification relates to the technical field of intelligent driving, in particular to a positioning method and device.
Background
The intelligentization of vehicles is an important component of artificial intelligence technology, and the role of the intelligentization of vehicles in social production and life is increasingly prominent, so that the intelligentization of vehicles becomes one of the main directions for guiding the development of traffic technology.
The positioning technology is a key technology of an unmanned vehicle and a vehicle with a driving assistance function (hereinafter, collectively referred to as a "vehicle"). At present, vehicle positioning mostly depends on GNSS/INS positioning technology. However, when in a complex urban environment, the examination of various complex conditions such as urban canyons, tunnels, underground garages, rain and fog can be performed, which brings a series of influences on the use of the GNSS/INS positioning technology, for example, in urban buildings, the GNSS/INS positioning cannot receive more than 4 satellites due to signal shielding, and thus the positioning accuracy is reduced.
Disclosure of Invention
The embodiments of the present disclosure provide a positioning method and apparatus, so as to partially solve the problems in the prior art.
The embodiment of the specification adopts the following technical scheme:
the present specification provides a positioning method, including:
the vehicle receives satellite data sent by a Global Navigation Satellite System (GNSS);
determining pseudo-range information between the vehicle and each satellite in the GNSS according to the time when the GNSS transmits the satellite data and the time when the vehicle receives the satellite data;
predicting the positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment to serve as predicted positioning information;
determining a satellite positioning error through GNSS positioning according to pseudo-range information between the vehicle and each satellite, position information of each satellite contained in the satellite data and the predicted positioning information for each satellite in the GNSS;
determining a sensing positioning error positioned through the sensing data according to the sensing data acquired by each sensor and the predicted positioning information;
and adjusting the predicted positioning information by adopting a preset optimization algorithm and minimizing the sum of the satellite positioning error and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
Optionally, determining pseudorange information between the vehicle and each satellite in the GNSS according to the time when the GNSS transmits the satellite data and the time when the vehicle receives the satellite data, specifically includes: for each satellite in the GNSS, determining a time difference between the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite according to the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite; and determining pseudo-range information between the vehicle and the satellite according to the product of the time difference and the preset propagation speed of the satellite data.
Optionally, for each satellite in the GNSS, determining a satellite positioning error through GNSS positioning according to pseudo-range information between the vehicle and the satellite, position information of the satellite included in the satellite data, and the predicted positioning information, specifically includes: for each satellite in the GNSS, determining an actual distance between the vehicle and the satellite according to the distance between the position information of the satellite contained in the satellite data and the predicted positioning information; determining an error of positioning through the satellite according to pseudo-range information between the vehicle and the satellite and an actual distance between the vehicle and the satellite;
and determining the satellite positioning error of the GNSS positioning according to the error of each satellite positioning.
Optionally, determining a sensing positioning error positioned by the sensing data according to the sensing data acquired by each sensor and the predicted positioning information, specifically including: for each sensor, acquiring image data corresponding to the current environment of the vehicle, wherein the image data is contained in sensing data acquired by the sensor; acquiring the position of an environment matched with the image data in an electronic map according to the image data corresponding to the current environment of the vehicle, and taking the position as the position information of the vehicle at the current moment; and determining a sensing positioning error positioned by the sensor according to the position information of the vehicle at the current moment and the predicted positioning information.
Optionally, the sensor is an inertial measurement unit IMU; according to the sensing data collected by each sensor and the predicted positioning information, determining the sensing positioning error positioned by the sensing data, which specifically comprises the following steps: acquiring motion information of the vehicle from the previous moment to the current moment, wherein the motion information is contained in sensing data acquired by the IMU; determining IMU positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment and the motion information of the vehicle from the previous moment to the current moment; and determining a sensing positioning error positioned by the sensing data of the IMU according to the IMU positioning information and the prediction positioning information.
Optionally, the preset optimization algorithm includes: the Levenberg-Marquardt LM algorithm.
Optionally, each sensor includes: at least one of radar and camera.
This specification provides a positioner, includes:
the receiving module is used for enabling the vehicle to receive satellite data sent by a Global Navigation Satellite System (GNSS);
the pseudo range determining module is used for determining pseudo range information between the vehicle and each satellite in the GNSS according to the time of the GNSS for sending the satellite data and the time of the vehicle for receiving the satellite data;
the prediction positioning module is used for predicting the positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment and taking the predicted positioning information as predicted positioning information;
a satellite positioning error determination module, configured to determine, for each satellite in the GNSS, a satellite positioning error that is obtained through GNSS positioning according to pseudo-range information between the vehicle and the satellite, position information of the satellite included in the satellite data, and the predicted positioning information;
the sensing positioning error determining module is used for determining the sensing positioning error positioned by the sensing data according to the sensing data acquired by each sensor and the predicted positioning information;
and the adjusting module is used for adjusting the predicted positioning information by adopting a preset optimization algorithm and minimizing the sum of the satellite positioning error and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described positioning method.
The present specification provides an unmanned device, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the positioning method when executing the program.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
in the embodiment of the specification, the real position information of each satellite in the GNSS is determined by acquiring satellite data transmitted by the GNSS, and pseudo range information between each satellite and the vehicle is determined according to the time difference of transmitting the satellite data to the vehicle and receiving the satellite data by the vehicle; and determining the satellite positioning error positioned by the GNSS according to the real position information of each satellite, the predicted positioning information of the vehicle at the current moment and the pseudo-range information between each satellite and the vehicle. And determining a sensing positioning error positioned by the sensing data according to the sensing data acquired by each sensor and the predicted positioning information of the vehicle at the current moment. And (3) adopting an optimization algorithm, minimizing the sum of the satellite and the sensing positioning error to serve as an optimization target, adjusting the predicted positioning information, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment. The embodiment of the specification not only enables the GNSS and each sensor to be mutually matched for positioning, but also makes full use of the GNSS and original data in each sensor to establish an error equation, and adopts an optimization algorithm to improve the positioning precision of the vehicle at the current moment, so that the vehicle can also realize higher positioning precision under the condition of only receiving less than 4 satellite data in the GNSS. When facing complex environment, the all-weather dead-angle-free accurate positioning of the vehicle can be realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
fig. 1 is a schematic flowchart of a positioning method provided in an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating an adjustment of predicted positioning information according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a positioning device provided in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an unmanned aerial vehicle provided in an embodiment of the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a positioning method provided in this specification, where the schematic flowchart includes:
s100: the vehicle receives satellite data transmitted by a global navigation satellite system GNSS.
In the process of driving, a receiver on the vehicle can acquire Satellite data transmitted by a Global Navigation Satellite System (GNSS). The satellite data is raw data of the satellite, and the raw data may specifically include satellite ephemeris data, such as position information of the satellite, velocity of the satellite, and other parameters. The time when the vehicle receives the satellite data can be defined as the current time, and the vehicle is positioned at the time by the scheme. The vehicle in the embodiment of the present disclosure may be an unmanned vehicle or a vehicle with a driving assistance function, and may of course be another type of vehicle, which is not limited in the embodiment of the present disclosure.
S102: and determining pseudo range information between the vehicle and each satellite in the GNSS according to the satellite data sending time of the GNSS and the satellite data receiving time of the vehicle.
The pseudorange information between the vehicle and each satellite in the GNSS may be determined based on a time difference between the time the GNSS transmits the satellite data and the time the vehicle receives the satellite data. In the illustrated embodiment, the pseudoranges are: during a GNSS positioning, the approximate distance between the receiver on the vehicle and each satellite. Because the satellite data is influenced by factors such as clock deviation between the vehicle and the satellite, atmospheric refraction and the like in the process of propagation, the pseudo-range information is not the true distance between the vehicle and the satellite, and a certain error exists.
S104: and predicting the positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment to serve as predicted positioning information.
In the embodiment of the present specification, the positioning information of the vehicle may be: world coordinates (x, y) where the vehicle is located, and the like. Since the positioning information of the vehicle at the previous moment is known, that is, the world coordinate where the vehicle was located at the previous moment is (x)0,y0) While the speed v of the vehicle at the previous moment is also known0The acceleration a of the vehicle at the previous time. Therefore, the vehicle positioning information (x) can be obtained according to the vehicle positioning information (x) at the last moment0,y0) And predicting the positioning information of the vehicle at the current moment.
If the vehicle can be predicted to move in a uniformly accelerated linear motion, the equation s (t) = v for the uniformly accelerated linear motion can be used0t+1/2at2Wherein s (t) represents displacement and t represents time; root of herbaceous plantAccording to the formula, the world coordinate (x) of the vehicle at the current moment is obtained1,y1) And positioning information is waited as the predicted positioning information. In the embodiments of the present specification, ρ may be usedpreRepresenting the predicted position location information at the current time. The specific calculation process for predicting the positioning information of the vehicle at the current moment by using the formula of uniform acceleration linear motion is the prior art, and is not repeated here. In addition, the positioning information of the vehicle at the current time may be predicted by using the uniform linear motion of the vehicle. Of course, the vehicle may also travel in other movement forms, and the manner of obtaining the vehicle positioning information at the current time may also be in other manners, which is not limited in this description embodiment.
S106: for each satellite in the GNSS, a satellite positioning error by GNSS positioning is determined based on pseudo range information between the vehicle and the satellite, position information of the satellite contained in satellite data, and predicted positioning information.
Since the vehicle is able to receive raw satellite data for each satellite in the GNSS, the position information for each satellite contained in the raw satellite data is true. For each satellite in the GNSS, the actual distance information between the vehicle and the satellite may be determined based on the true position information and the predicted positioning information of the satellite contained in the satellite data. And determining a satellite positioning error through GNSS positioning based on an error between pseudo-range information (having a certain error) between the vehicle and the satellite and actual distance information between the vehicle and the satellite.
S108: and determining the sensing positioning error positioned by the sensing data according to the sensing data acquired by each sensor and the predicted positioning information.
The sensor may be a radar, a camera, an Inertial Measurement Unit (IMU), or the like. The sensors may be mounted on the vehicle for collecting various information of the vehicle, such as information related to the motion state and positioning of the vehicle. The positioning information of the vehicle at the current moment can be obtained by analyzing the original sensing data acquired by each sensor. The sensing positioning error of the positioning of the sensing data collected by each sensor can be determined by the positioning information of the vehicle at the current time (i.e., the real sensor positioning information of the vehicle at the current time) acquired by each sensor and the error between the predicted positioning information.
S110: and adjusting the predicted positioning information by adopting a preset optimization algorithm and minimizing the sum of the satellite positioning error and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
A predetermined optimization algorithm, such as Levenberg-Marquardt (LM) algorithm, may be used to minimize the sum of the satellite positioning error (i.e., the error between the pseudorange between the satellite and the vehicle and the actual distance between the satellite and the vehicle) and the sensed positioning error (i.e., the error between the predicted positioning information of the vehicle at the current time and the actual sensor positioning information of the vehicle at the current time) to optimize the target, i.e., to adjust the predicted positioning information to minimize the error between the integrated real data and the predicted data, so that the adjusted predicted positioning information can be closer to the real positioning information. Therefore, the adjusted predicted positioning information can be used as the positioning information of the vehicle at the current time.
Through the embodiment of the specification, even if the receiver on the vehicle cannot receive satellite data of more than 4 satellites, optimization can be performed through the optimization algorithm, so that more accurate positioning information of the vehicle at the current moment can be obtained. The embodiment of the specification not only enables the GNSS and each sensor to be mutually matched for positioning, but also makes full use of the GNSS and the original data in each sensor to establish an error equation, and improves the positioning precision of the vehicle at the current moment by adopting an optimization algorithm.
In S102 of fig. 1, the pseudo-range information may be obtained by: for each satellite in the GNSS, determining a time difference delta T between the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite according to the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite. According to the time difference Delta T and the preset propagation speed V of the satellite dataXDetermining pseudorange information between the vehicle and the satelliteρPseudorange=VXΔ T. The preset propagation speed of the satellite data may be a speed of light.
In S106 of fig. 1, the satellite positioning error may be obtained by: for each satellite in the GNSS, the position information of the satellite contained in the satellite data and the predicted positioning information rho are usedpreDetermining the actual distance ρ between the vehicle and the satelliteMeasurement of. According to pseudo-range information rho between the vehicle and the satellitePseudorangeAnd the actual distance ρ between the vehicle and the satelliteMeasurement ofError of the satellite, determining the error of the positioning by the satellitex=ρPseudorange-ρMeasurement of. And according to the error of each satellite positioningxDetermining a satellite positioning error by GNSS positioninggnss. It should be noted that the satellite positioning error through GNSS positioning is errorgnssError for positioning each satellitexOr the sum of (d) and (d) may also be the error of each satellite positioningxOf course, the mean value of (b) may be expressed in other ways, and the embodiment of the present specification is not limited to this.
In S108 of fig. 1, if the sensor includes: at least one of radar and camera. Then, for this type of sensor, the acquisition mode of the sensing positioning error may specifically be: and acquiring image data corresponding to the current environment of the vehicle, which is contained in the sensing data acquired by each sensor. According to image data corresponding to the current environment of the vehicle, the position of the environment matched with the image data in the electronic map is obtained and used as the position information rho of the vehicle at the current momentmea. According to the position information rho of the vehicle at the current momentmeaAnd predicted positioning information rhopreError between, determining a sensed positioning error of positioning by the sensorpoint=ρpre-ρmea。
If the sensor is a camera, the image data corresponding to the current environment of the vehicle may be a visual image. If the sensor is a radar, the image data corresponding to the current environment of the vehicle may be point cloud data. When the image data isDuring point cloud data, the position of the environment matched with the image data in a preset point cloud map can be obtained according to the point cloud data corresponding to the current environment of the vehicle and used as the position information rho of the vehicle at the current momentmea。
Additionally, the sensor may also be an IMU. The method for determining the sensing positioning error through the sensing data acquired by the IMU may specifically be: and acquiring motion information of the vehicle from the previous moment to the current moment, wherein the motion information is included in the sensing data acquired by the IMU, and the motion information can be information such as acceleration, angular velocity and the like of the vehicle. According to the positioning information of the vehicle at the previous moment and the motion information of the vehicle from the previous moment to the current moment, the IMU positioning information rho of the vehicle at the current moment can be calculatedimu. Note that, the IMU positioning information ρ of the vehicle at the current time is acquiredimuThe specific calculation process of (a) is the prior art, and is not described in detail here. Positioning information rho according to IMUimuAnd predicted positioning information rhopreError therebetween, a sensed positioning error determined for positioning of sensed data by the IMUimu=ρpre-ρimu。
Continuing with the above example, the error of positioning by each satellite in the GNSS is obtainedx=ρPseudorange-ρMeasurement ofObtaining the satellite positioning error through GNSS positioninggnss(ii) a Acquiring a sensing positioning error for positioning by means of a sensor of the radar and/or camera typepoint=ρpre-ρmea(ii) a Also obtains the sensing positioning error of the sensing data positioning through the IMUimu=ρpre-ρimu. The LM algorithm can be employed, using the formula: f (p) = errorgnss+N*errorpoint+errorimu=M*(ρPseudorange-ρMeasurement of)+N*(ρpre-ρmea)+(ρpre-ρimu) And acquiring the sum of the satellite positioning error and the sensing positioning error. In this formula, M represents the number of satellites and N represents the number of sensors of the radar and/or camera type. The sum f (p) of the satellite positioning error and the sensing positioning error obtained by the formula is minimized to be an optimization target, and the pre-determination is adjustedBit information rhopreAnd the adjusted predicted positioning information is used as the positioning information of the vehicle at the current moment.
When the positioning information of the vehicle at the current moment is optimized through the LM algorithm, the limiting condition can be further increased. Specifically, any fixed target object in the surrounding environment of the vehicle is determined; for any sensor, determining the position of the fixed target object and the distance between the vehicle and the fixed target object at the current moment according to the image data of the fixed target object and the vehicle at the current moment contained in the sensing data acquired by the sensor; determining the distance between the satellite and the fixed target object according to the position of the fixed target object and the position of the satellite contained in the known satellite data; and adjusting the predicted positioning information by taking the minimum sum of the satellite positioning error and the sensing positioning error and the condition that the pseudo range between the vehicle and the satellite, the distance between the satellite and the fixed target and the distance between the vehicle and the fixed target meet the triangle rule limiting condition as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
Further, the distance between the adjusted predicted position information and the unadjusted predicted position information may be made less than a specified threshold.
As shown in fig. 2, the above-mentioned limitation is exemplified by taking a two-dimensional world coordinate system as an example of the satellite, the fixed target, and the vehicle. Defining the coordinates of the satellite as OStar (star)Defining the coordinate of the fixed object at the current time as OArticle (A)Defining the coordinates of the predicted vehicle at the current time as Opre. The pseudorange between the vehicle and the satellite is known as pPseudorangeThe distance between the satellite and the fixed target is LStar objectThe distance between the vehicle and the fixed target is LVehicle articleTo make OStar (star)、OArticle (A)Remains fixed, pPseudorange、LStar object、LVehicle articleSatisfy the triangle rule, adjust OprePosition of (2) to be adjusted OpreAs the positioning information of the vehicle at the present time. In addition, in adjusting OpreIn the course of the position,the adjustment amplitude cannot be too large, otherwise it will be distorted. Specifically, the adjusted O can be usedpreWith unregulated OpreThe distance between is less than a specified threshold, which may be set to 2-3 meters.
The embodiment of the specification determines the real position information of each satellite in the GNSS by acquiring satellite data transmitted by the GNSS, and determines pseudo range information between each satellite and the vehicle according to the product of the time difference of transmitting the satellite data to the vehicle and receiving the satellite data by the vehicle and the preset propagation speed of the satellite data. For each satellite in the GNSS, determining the actual distance between the vehicle and the satellite through the distance between the real position information of the satellite and the predicted positioning information of the vehicle at the current moment, and determining the satellite positioning error through GNSS positioning according to the error between the pseudo-range information between each satellite and the vehicle and the actual distance between the vehicle and the satellitegnss. In addition, the method also determines the sensing positioning information of the vehicle at the current moment according to the sensing data collected by each sensor (radar, camera, IMU), and determines the sensing positioning error positioned by the sensing data, namely N × error, according to the error between the sensing positioning information and the predicted positioning information of the vehicle at the current momentpoint+errorimu. Adopting an optimization algorithm, and taking the sum of the satellite and the sensing positioning error f (p) = errorgnss+N*errorpoint+errorimuAnd (4) minimizing to an optimization target, adjusting the predicted positioning information, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment. The embodiment of the specification not only enables the GNSS and each sensor to be mutually matched for positioning, but also makes full use of the GNSS and original data in each sensor to establish an error equation, and adopts an optimization algorithm to improve the positioning precision of the vehicle at the current moment, so that the vehicle can also realize higher positioning precision under the condition of only receiving less than 4 satellite data in the GNSS. When facing complex environment, the all-weather dead-angle-free accurate positioning of the vehicle can be realized.
The positioning method provided by the specification can be particularly applied to accurate positioning of unmanned vehicles and vehicles with driving assisting functions in complex urban environments. The unmanned vehicle can be an unmanned distribution vehicle, and the unmanned distribution vehicle can be applied to the field of distribution by using the unmanned distribution vehicle, such as the distribution scene of express delivery, takeaway and the like by using the unmanned distribution vehicle. Specifically, in the above-described scenario, delivery may be performed using an autonomous vehicle fleet configured with a plurality of unmanned delivery vehicles.
Based on the same idea, the positioning method provided by the embodiment of the present specification further provides a corresponding apparatus, a storage medium, and an unmanned device.
Fig. 3 is a schematic structural diagram of a positioning device provided in an embodiment of the present disclosure, where the device includes:
a receiving module 200, configured to enable a vehicle to receive satellite data sent by a global navigation satellite system GNSS;
a pseudo-range determining module 202, configured to determine pseudo-range information between the vehicle and each satellite in the GNSS according to the time when the GNSS sends the satellite data and the time when the vehicle receives the satellite data;
the predicted positioning module 204 is configured to predict, according to the positioning information of the vehicle at the previous time, the positioning information of the vehicle at the current time as predicted positioning information;
a satellite positioning error determination module 206, configured to determine, for each satellite in the GNSS, a satellite positioning error through GNSS positioning according to pseudo-range information between the vehicle and the satellite, position information of the satellite included in the satellite data, and the predicted positioning information;
a sensing positioning error determining module 208, configured to determine, according to the sensing data acquired by each sensor and the predicted positioning information, a sensing positioning error that is positioned by the sensing data;
the adjusting module 210 is configured to adjust the predicted positioning information by using a preset optimization algorithm and minimizing a sum of the satellite positioning error and the sensing positioning error as an optimization target, and use the adjusted predicted positioning information as the positioning information of the vehicle at the current time.
Optionally, the pseudo-range determining module 202 is specifically configured to determine, for each satellite in the GNSS, a time difference between a time when the GNSS transmits satellite data of the satellite and a time when the vehicle receives the satellite data of the satellite according to the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite; and determining pseudo-range information between the vehicle and the satellite according to the product of the time difference and the preset propagation speed of the satellite data.
Optionally, the satellite positioning error determining module 206 is specifically configured to determine, for each satellite in the GNSS, an actual distance between the vehicle and the satellite according to a distance between the position information of the satellite included in the satellite data and the predicted positioning information; determining an error of positioning through the satellite according to pseudo-range information between the vehicle and the satellite and an actual distance between the vehicle and the satellite; and determining the satellite positioning error of the GNSS positioning according to the error of each satellite positioning.
Optionally, the sensing and positioning error determining module 208 is specifically configured to, for each sensor, obtain image data corresponding to the current environment of the vehicle, included in the sensing data acquired by the sensor; acquiring the position of an environment matched with the image data in an electronic map according to the image data corresponding to the current environment of the vehicle, and taking the position as the position information of the vehicle at the current moment; and determining a sensing positioning error positioned by the sensor according to the position information of the vehicle at the current moment and the predicted positioning information.
Optionally, the sensor is an inertial measurement unit IMU. The sensing and positioning error determining module 208 is further configured to obtain motion information of the vehicle from a previous time to a current time, which is included in the sensing data acquired by the IMU; determining IMU positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment and the motion information of the vehicle from the previous moment to the current moment; and determining a sensing positioning error positioned by the sensing data of the IMU according to the IMU positioning information and the prediction positioning information.
The present specification also provides a computer readable storage medium, which stores a computer program, which when executed by a processor is operable to perform a positioning method as provided in fig. 1 above.
Based on the positioning method shown in fig. 1, the embodiment of the present specification further provides a schematic structural diagram of the unmanned device shown in fig. 4. As shown in fig. 4, the drone includes, at the hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile memory, although it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the positioning method described in fig. 1 above.
Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims (10)

1. A method of positioning, comprising:
the vehicle receives satellite data sent by a Global Navigation Satellite System (GNSS);
determining pseudo-range information between the vehicle and each satellite in the GNSS according to the time when the GNSS transmits the satellite data and the time when the vehicle receives the satellite data;
predicting the positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment to serve as predicted positioning information;
determining a satellite positioning error through GNSS positioning according to pseudo-range information between the vehicle and each satellite, position information of each satellite contained in the satellite data and the predicted positioning information for each satellite in the GNSS;
determining a sensing positioning error positioned through the sensing data according to the sensing data acquired by each sensor and the predicted positioning information;
and adjusting the predicted positioning information by adopting a preset optimization algorithm and minimizing the sum of the satellite positioning error and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
2. The method as claimed in claim 1, wherein determining pseudorange information between said vehicle and each satellite of said GNSS based on a time of transmission of said satellite data by said GNSS and a time of reception of said satellite data by said vehicle comprises:
for each satellite in the GNSS, determining a time difference between the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite according to the time when the GNSS transmits the satellite data of the satellite and the time when the vehicle receives the satellite data of the satellite;
and determining pseudo-range information between the vehicle and the satellite according to the product of the time difference and the preset propagation speed of the satellite data.
3. The method according to claim 1, wherein determining, for each satellite in the GNSS, a satellite positioning error through GNSS positioning based on pseudo-range information between the vehicle and the satellite, position information of the satellite contained in the satellite data, and the predicted positioning information, comprises:
for each satellite in the GNSS, determining an actual distance between the vehicle and the satellite according to the distance between the position information of the satellite contained in the satellite data and the predicted positioning information;
determining an error of positioning through the satellite according to pseudo-range information between the vehicle and the satellite and an actual distance between the vehicle and the satellite;
and determining the satellite positioning error of the GNSS positioning according to the error of each satellite positioning.
4. The method of claim 1, wherein determining a sensing positioning error for positioning via the sensing data based on the sensing data collected by each sensor and the predicted positioning information comprises:
for each sensor, acquiring image data corresponding to the current environment of the vehicle, wherein the image data is contained in sensing data acquired by the sensor;
acquiring the position of an environment matched with the image data in an electronic map according to the image data corresponding to the current environment of the vehicle, and taking the position as the position information of the vehicle at the current moment;
and determining a sensing positioning error positioned by the sensor according to the position information of the vehicle at the current moment and the predicted positioning information.
5. The method of claim 1, wherein the sensor is an Inertial Measurement Unit (IMU);
according to the sensing data collected by each sensor and the predicted positioning information, determining the sensing positioning error positioned by the sensing data, which specifically comprises the following steps:
acquiring motion information of the vehicle from the previous moment to the current moment, wherein the motion information is contained in sensing data acquired by the IMU;
determining IMU positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment and the motion information of the vehicle from the previous moment to the current moment;
and determining a sensing positioning error positioned by the sensing data of the IMU according to the IMU positioning information and the prediction positioning information.
6. The method of claim 1, wherein the predetermined optimization algorithm comprises: the Levenberg-Marquardt LM algorithm.
7. The method of claim 4, wherein the sensors comprise: at least one of radar and camera.
8. A positioning device, comprising:
the receiving module is used for enabling the vehicle to receive satellite data sent by a Global Navigation Satellite System (GNSS);
the pseudo range determining module is used for determining pseudo range information between the vehicle and each satellite in the GNSS according to the time of the GNSS for sending the satellite data and the time of the vehicle for receiving the satellite data;
the prediction positioning module is used for predicting the positioning information of the vehicle at the current moment according to the positioning information of the vehicle at the previous moment and taking the predicted positioning information as predicted positioning information;
a satellite positioning error determination module, configured to determine, for each satellite in the GNSS, a satellite positioning error that is obtained through GNSS positioning according to pseudo-range information between the vehicle and the satellite, position information of the satellite included in the satellite data, and the predicted positioning information;
the sensing positioning error determining module is used for determining the sensing positioning error positioned by the sensing data according to the sensing data acquired by each sensor and the predicted positioning information;
and the adjusting module is used for adjusting the predicted positioning information by adopting a preset optimization algorithm and minimizing the sum of the satellite positioning error and the sensing positioning error as an optimization target, and taking the adjusted predicted positioning information as the positioning information of the vehicle at the current moment.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.
10. An unmanned aerial device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any of claims 1-7.
CN202010210131.8A 2020-03-24 2020-03-24 Positioning method and device Active CN111077555B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010210131.8A CN111077555B (en) 2020-03-24 2020-03-24 Positioning method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010210131.8A CN111077555B (en) 2020-03-24 2020-03-24 Positioning method and device

Publications (2)

Publication Number Publication Date
CN111077555A true CN111077555A (en) 2020-04-28
CN111077555B CN111077555B (en) 2020-08-07

Family

ID=70324643

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010210131.8A Active CN111077555B (en) 2020-03-24 2020-03-24 Positioning method and device

Country Status (1)

Country Link
CN (1) CN111077555B (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337961A (en) * 2020-05-21 2020-06-26 深圳市西博泰科电子有限公司 Method, device, equipment and medium for improving positioning accuracy of vehicle in city
CN111812698A (en) * 2020-07-03 2020-10-23 北京图森未来科技有限公司 Positioning method, device, medium and equipment
CN111999745A (en) * 2020-05-21 2020-11-27 深圳市西博泰科电子有限公司 Data processing method, device and equipment for improving landmark positioning precision
CN112019997A (en) * 2020-08-05 2020-12-01 锐捷网络股份有限公司 Vehicle positioning method and device
WO2022218306A1 (en) * 2021-04-12 2022-10-20 北京三快在线科技有限公司 Unmanned driving device
CN115267861A (en) * 2022-09-30 2022-11-01 智道网联科技(北京)有限公司 Automatic driving fusion positioning precision testing method and device and electronic equipment
CN115856979A (en) * 2023-02-16 2023-03-28 智道网联科技(北京)有限公司 Positioning method and device for automatic driving vehicle, electronic equipment and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102436004A (en) * 2007-09-04 2012-05-02 联发科技股份有限公司 Positioning system and method thereof
US20150177003A1 (en) * 2013-12-25 2015-06-25 Industrial Technology Research Institute Vehicle position calibration method and apparatus
CN108226980A (en) * 2017-12-23 2018-06-29 北京卫星信息工程研究所 Difference GNSS and the adaptive close coupling air navigation aids of INS based on Inertial Measurement Unit
CN108700670A (en) * 2016-03-09 2018-10-23 三菱电机株式会社 Positioning device and localization method
CN109471144A (en) * 2018-12-13 2019-03-15 北京交通大学 Based on pseudorange/pseudorange rates multisensor tight integration train combined positioning method
CN109945860A (en) * 2019-05-07 2019-06-28 深圳市联和安业科技有限公司 A kind of INS and DR inertial navigation method and system based on satellite tight integration

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102436004A (en) * 2007-09-04 2012-05-02 联发科技股份有限公司 Positioning system and method thereof
US20150177003A1 (en) * 2013-12-25 2015-06-25 Industrial Technology Research Institute Vehicle position calibration method and apparatus
CN108700670A (en) * 2016-03-09 2018-10-23 三菱电机株式会社 Positioning device and localization method
CN108226980A (en) * 2017-12-23 2018-06-29 北京卫星信息工程研究所 Difference GNSS and the adaptive close coupling air navigation aids of INS based on Inertial Measurement Unit
CN109471144A (en) * 2018-12-13 2019-03-15 北京交通大学 Based on pseudorange/pseudorange rates multisensor tight integration train combined positioning method
CN109945860A (en) * 2019-05-07 2019-06-28 深圳市联和安业科技有限公司 A kind of INS and DR inertial navigation method and system based on satellite tight integration

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337961A (en) * 2020-05-21 2020-06-26 深圳市西博泰科电子有限公司 Method, device, equipment and medium for improving positioning accuracy of vehicle in city
CN111999745A (en) * 2020-05-21 2020-11-27 深圳市西博泰科电子有限公司 Data processing method, device and equipment for improving landmark positioning precision
CN111812698A (en) * 2020-07-03 2020-10-23 北京图森未来科技有限公司 Positioning method, device, medium and equipment
CN112019997A (en) * 2020-08-05 2020-12-01 锐捷网络股份有限公司 Vehicle positioning method and device
WO2022218306A1 (en) * 2021-04-12 2022-10-20 北京三快在线科技有限公司 Unmanned driving device
CN115267861A (en) * 2022-09-30 2022-11-01 智道网联科技(北京)有限公司 Automatic driving fusion positioning precision testing method and device and electronic equipment
CN115267861B (en) * 2022-09-30 2023-03-10 智道网联科技(北京)有限公司 Automatic driving fusion positioning precision testing method and device and electronic equipment
CN115856979A (en) * 2023-02-16 2023-03-28 智道网联科技(北京)有限公司 Positioning method and device for automatic driving vehicle, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN111077555B (en) 2020-08-07

Similar Documents

Publication Publication Date Title
CN111077555B (en) Positioning method and device
JP6643883B2 (en) GPS drift processing method and system
CN111311902B (en) Data processing method, device, equipment and machine readable medium
CN111238450B (en) Visual positioning method and device
CN109564292B (en) Method and system for position determination
CN111288971B (en) Visual positioning method and device
CN111797906B (en) Method and device for positioning based on vision and inertial mileage
CN111026081B (en) Error calculation method, device, equipment and storage medium
WO2020184013A1 (en) Vehicle control device
CN112712558A (en) Positioning method and device of unmanned equipment
CN113109851A (en) Abnormity detection method and device, storage medium and electronic equipment
CN113674424B (en) Method and device for drawing electronic map
CN110530398B (en) Method and device for detecting precision of electronic map
CN112859131B (en) Positioning method and device of unmanned equipment
CN113048989B (en) Positioning method and positioning device of unmanned equipment
CN111798489B (en) Feature point tracking method, device, medium and unmanned equipment
CN114299147A (en) Positioning method, positioning device, storage medium and electronic equipment
CN116300842A (en) Unmanned equipment control method and device, storage medium and electronic equipment
CN112712561A (en) Picture construction method and device, storage medium and electronic equipment
CN112393723B (en) Positioning method, positioning device, medium and unmanned equipment
WO2023185215A1 (en) Data calibration
US20230003903A1 (en) Apparatus, system and method for providing global localization output and application of same
CN116358557A (en) High-precision positioning information determining method, fusion positioning module and high-precision map engine
CN116242385A (en) Visual navigation data calibration method and device
CN116184466A (en) Method and device for determining landing point of unmanned aerial vehicle

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant