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Behind the first lane level navigation in the industry

2020-11-06 20:07:11 amap_tech

Reading guide

10 month 30 Japan , Huawei united with Gaode 、 Qianxun released the first lane level navigation application for mobile phone users in the industry . Behind this is the continuous development of high-precision positioning technology , Finally, it is the process of mature mass production . This paper will combine the work of gaude map in the field of lane level navigation and automatic driving , Share our thoughts on the evolution of high precision positioning technology , And some practices in the practical application of high precision positioning .

 

One 、 An overview of Golder positioning technology

Positioning technology is to support the navigation of Gaud map 、 Key basic technologies of core business such as transportation , His main task is to identify objects ( Usually people or cars ) Position and posture in a relatively fixed coordinate system . We use the mobile phone gaude map as an example to illustrate which technologies play a role in the actual application scenarios . 

Generally, the basic positioning ability of mobile phone is determined by the mobile phone GNSS Chip provides , It provides us with... For most of the outdoor scenes 5~10 Meter positioning accuracy . But when the satellite signal is bad , Positioning may drift , We need to identify this situation . in addition , When the signal is disturbed , There may be a regular shift in position , We also need to identify interference , And if possible, find the right position again . When GNSS When it's not in place , Want to keep positioning , You can use sensors to recognize walking / Driving state , Then calculate the course (PDR、VDR). When you enter the room, the satellite signal is lost , The common positioning method is based on the base station scanned by the mobile phone and Wifi、 Bluetooth and other signals do network positioning .

These techniques provide the basic location coordinates , And in the navigation process , We are more concerned about which road we are driving on , There is no yaw , How far is it from the next intersection , To get this information, you need to use map matching technology . In some very complex situations , Such as viaduct 、 Main and auxiliary roads , It becomes very difficult to judge the path , At this time, we need to use some special recognition model to solve the matching problem .

 

chart 1 Location technology in mobile phone Gaud map

The above is only part of the technology in a specific business scenario , Here's a more complete picture of Golder's positioning technology . In general , We're building a set of “ cloud + End + data ” The complete technical system of , And build a quality iteration system to ensure the continuous updating and iteration of each technical module , To support numerous positioning business applications .

 

chart 2 Big picture of Golder positioning technology

Two 、 How positioning technology evolves to high precision

Back to the picture 1, We can see that the location technology mentioned here extends the user scenario , But there is no obvious improvement in positioning accuracy . If you want to implement the lane level navigation mentioned above , Even more intelligent automatic driving , The positioning accuracy is required to be significantly improved to sub meter level , Even centimeter scale . So how to do this ? We will do an analysis from the perspective of technology .

First , Let's take a picture 2 Involved in the , And other, broader positioning technologies , According to the principle of positioning, it can be divided into three categories , They are dead reckoning 、 Geometric location and feature location . According to different positioning types, the factors affecting the accuracy are analyzed , Summarize the methods to improve the accuracy , Finally, we hope to find the technical path to achieve high-precision positioning .

surface 1 Analysis and summary of different positioning technologies

1. Dead reckoning

The basic principle of dead reckoning is to start from the position at the last moment , According to the direction of motion and distance to calculate the position of the next moment . Obviously, this positioning method requires a known starting position , Otherwise, we can only get the relative position change . At the same time, in the calculation process, the positioning error will continue to increase , So the direct factor affecting the accuracy is to calculate the time or distance .

Besides , The calculation accuracy is also affected by the measurement accuracy at each time , For inertial sensors , This is directly determined by the accuracy of inertial devices . for example , Strategic inertial navigation with the highest accuracy , The position error divergent with time can reach 30m/hr, by comparison , The precision of tactical inertial navigation is poor 3 An order of magnitude , And the consumer grade micromachines we use (MEMS) Inertial navigation accuracy is worse than tactical level 1~2 An order of magnitude .

In addition to device accuracy , The accuracy of the model in the calculation process will also affect the positioning accuracy , There are two aspects to this : One is the compensation model of the device measurement error , The second is the compensation model of calculation error . Usually only when the accuracy of the device itself is high enough , A more accurate compensation model needs to be considered .

2. Geometric positioning

Geometric positioning is to measure the range or angle of the reference equipment with known position , Then through geometric calculation to determine their own position . According to the way of geometric calculation , Include RSS( Signal strength )、TOA( Arrival time )、AOA( Arrival angle )、TDOA( Arrival time difference ) And so on . For angular positioning method , A small angle measurement error may produce a large position error in a distance from the positioning facility , So this method ( If AOA Method of Bluetooth location ) The positioning accuracy is usually limited by the range . In the ranging method , Using the time arrival model ( If TOA Methodical GNSS location , use TDOA Methodical UWB location ) Specific signal strength model ( If RSS Methods of Bluetooth and Wifi location ) More likely to achieve higher positioning accuracy . But in practice , The final positioning accuracy is affected by the accuracy of distance measurement , Especially in the scene of long-distance signal transmission such as satellite positioning , How to eliminate the measurement error in the signal propagation path , It becomes the key to determine the positioning accuracy . Besides , The accuracy of geometric positioning is also affected by the number and distribution of positioning facilities , The more facilities are observed at the same time 、 The more evenly distributed , The higher the accuracy is .

3. Feature location

The feature location method first obtains some features of the surrounding environment , Such as base station id、Wifi The fingerprint 、 Geomagnetic field 、 Images 、Lidar Point cloud, etc . There are two ways to deal with it , One is to match the received features with the feature maps collected in advance , Locate in the feature map ; The other is no feature map , Position and attitude estimation is carried out by comparing the feature changes of the front and back frames ( namely SLAM technology ), To achieve the relative positioning effect similar to dead reckoning . obviously , The direct factor affecting the accuracy of feature location is the number of features 、 Quality and discrimination .

therefore , Using signal fingerprint features ( Such as Wifi The fingerprint ) Because of the sparsity of the fingerprint, the accuracy of the method is usually limited . The location method based on environment perception features is in the case of intensive features ( Such as the number of high lines Lidar, Medium and high resolution images, etc ) It can achieve high precision , But in practical application, it is greatly affected by the environment , When the environmental characteristics are single ( Like the sky 、 snowy day ) The accuracy will be reduced, even unable to locate . in addition , The positioning accuracy of feature map matching method is also limited by the accuracy of feature map , Feature estimation method ( Like vision SLAM) The positioning error will accumulate with distance , It has a divergence effect similar to dead reckoning .

Combine the above analysis , It can screen out the technology selection with high precision positioning ability . The complete high precision positioning scheme requires at least one high precision absolute positioning technology , As in geometric positioning GNSS location , In feature location Lidar Point cloud matching, etc ; secondly , For the scenario constraints in these scenarios , Relative positioning means , Such as DR、SLAM Technology, etc. , To supplement . 

surface 2 High precision positioning technology line

3、 ... and 、 High precision positioning of business scenarios and solutions

From the technical point of view, the possible implementation path of high-precision positioning is analyzed , Next, we will start from the specific business scenarios of Golder , Take a look at how these technologies are implemented in the actual business .

1. What kind of high-precision positioning capability is needed in actual business scenarios

Travel scene is the core business scene of goldmap . Take driving as an example , The traditional application is TBT(Turn-by-turn) Navigation is representative of road level applications , Its requirements for positioning accuracy are as follows 10m about . More sophisticated navigation experience , Such as lane level navigation , The car needs to be positioned in the driveway , This requires position accuracy to reach 1 Within meters . For smart driving scenarios , In order to ensure the safety of automatic driving of the machine , Higher requirements for positioning accuracy , Generally, the accuracy in the transverse direction of the road needs to be less than 20 centimeter .

chart 3 High precision application of driving travel scene

In addition to the requirement of accuracy , Different business scenarios also require other dimensions for high precision positioning capabilities .

1) reliability ( Or integrity ): This is mainly used to measure whether the positioning system has the ability to detect possible errors , This is particularly important for applications that rely on positioning for intelligent driving . such as , The system needs to give an accuracy radius for the current position , When the actual position accuracy is less than this radius , The system is reliable . therefore , For applications with high reliability requirements , The estimation of this radius is usually conservative .

2) Usability : If the system can accurately judge the current positioning accuracy to meet the navigation 、 Automatic driving and other business requirements , Then the system is available . obviously , Availability requires that the estimation of the accuracy radius should not be too large , Otherwise, the system will frequently think that positioning is unreliable , The related functions cannot be used .

3) Calculate the force : As an upgrade of traditional navigation applications , Lane level navigation is highly sensitive to computational power , Usually required to meet the current mobile phone 、 The computational power limitation of vehicle navigation . Intelligent driving is classified according to different degrees of intelligence (SAE Level1~Level5), The requirements for computational power are also different . Usually, the traditional vehicle electrical architecture of low-level intelligent driving can not provide more computing resources , The centralized computing unit used in high-level intelligent driving can provide more abundant computing resources .

In addition, there are many requirements related to practical application , For example, the time stability of the output positioning results , Locate the scope of the scene that can be covered .

To sum up , The core capability required for lane level navigation is to identify the current Lane , This generally requires positioning accuracy less than 1 rice , At the same time as a navigation application , It is necessary to improve the accuracy of road matching on the basis of traditional navigation . Low level intelligent driving (L3 following ) Lane recognition and road matching are required ( This is mainly to ensure that intelligent driving can only be opened within the permitted road range , Like the highway ) It's more accurate , Furthermore, it is required that the accuracy of the horizontal position should reach 20 centimeter , In addition, the reliability of the system is required to be higher .

The above two types of applications are our main business scenarios at present , Both of them require high precision positioning function under the condition of low computational power . To meet the needs of these businesses , We have developed a lightweight integrated positioning engine .

2. Lightweight integrated integration positioning solution

The positioning technology we use here mainly includes :RTK-GNSS technology , Image semantic matching ,IMU Or a car model DR Technology, etc. . among , Image semantic matching uses visual devices ( It's usually a smart camera on the car , Such as mobileye etc. ) Lane lines identified 、 Ground signs and other information as input , Matching and positioning with high-precision map data , This process deals with limited semantic elements , So it doesn't cost much . As for other technologies, they have been involved in traditional navigation and positioning , Therefore, the overall computational power consumption of the scheme can be controlled at the same level as the ordinary navigation .

chart 4 Integrated integration positioning solution

The above integrated scheme framework can receive all or part of the positioning signal input , At the same time, it provides the location results of road level and lane level , Ensure the continuity of positioning in the whole scene . When landing in a specific application , The form of the scheme is different .

For intelligent driving applications , High reliability requires more redundant information for fault tolerance , So it's usually necessary to RTK-GNSS、IMU、 Visual semantics and all the information input , After receiving this information, two problems need to be solved :1) How to judge which input signals are reliable ,2) How to make full use of all information for fusion positioning .

Here we use particle filter based on multi hypothesis as the basic algorithm of high precision fusion location , And the design and implementation of the following algorithm improvement :

  • Reduce the particle dimension according to the assumed characteristics , Reduce computation ; 

  • Hierarchical normalization is used to solve the problem of particle degradation caused by small system errors ; 

  • Context based posterior confidence calculation , Solve the missing or wrong confidence level of input signal ;

  • Input signal delay and out of sequence processing based on signal window ;

  • Using high-precision satellite positioning and high-precision map data to assist sensor calibration , promote DR Ability .

At present, the algorithm has been used in a L3 Level intelligent driving vehicle landing , Large scale experiments are being carried out .

For lane level navigation applications , Due to the limitation of cost and usage conditions , It's usually not possible to get all the input information . But according to the table 2, We need at least RTK-GNSS Or one of the visual semantics for high-precision absolute positioning . On the mobile terminal , The more convenient solution is to upgrade the mobile phone GNSS Chip support RTK Difference to improve accuracy . The first lane level navigation application on Huawei mobile phone is to integrate Huawei and Qianqian search technology and services to achieve high-precision absolute positioning .

For the car machine , Intelligent camera information used in low-level intelligent driving function can be connected to navigation , Direct upgrade of lane level navigation function . The high precision fusion location in this application scenario is still based on the above particle filter algorithm , But it is necessary to adapt various input signal types and signal characteristics flexibly in algorithm and engineering .

in addition , According to the characteristics of the navigation scene , The integrated fusion positioning is also based on the lane level positioning results , Like whether the car is on the exit lane , To help judge the main and auxiliary roads 、 Yaw under complex road conditions such as viaduct , Enhance the overall experience of user navigation .

At present, the lane level navigation based on Huawei mobile phone will be released online , The vehicle lane level project is also being implemented , I'll see you soon .

3. Multiple tight coupling for complex scenes SLAM technology

The above lightweight fusion positioning scheme can solve most of the outdoor occlusion 、 The problem of high-precision location of semantic clear scene , But for more complex scenarios , Like indoors 、 The parking lot 、 City complex intersection and so on , High precision GNSS May not be valid , Visual semantic information is also less , At this time, we need to integrate more abundant positioning means . At a high level of intelligent driving (L4 above ) In common use Lidar Point cloud matching , and / Or high precision inertial navigation DR To ensure continuous high-precision positioning , but Lidar And high precision inertial navigation costs a lot , Large scale applications are limited .SLAM Technology can use low-cost visual sensors , Continue to calculate high-precision position and attitude , It can be used as an effective means of low-cost and high-precision positioning . Compared to the lightweight scheme above , It's computationally expensive , But in the current environment of continuous upgrading of terminal computing power , Still has good landing potential .

Our idea is to use a tightly coupled scheme , Make the best use of all kinds of low-cost sensors :GNSS、IMU、 Information provided by vision, etc , According to the error characteristics of these information in different dimensions , Build an optimization model , To achieve the optimal position and attitude estimation .

chart 5 Multivariate tight coupling SLAM Algorithm framework

We're making datasets public EuRoc and Kaist This set of tight coupling is compared on SLAM Algorithms and the current popular vision -IMU Fusion algorithm 、 Vision -IMU-GNSS The effect of fusion algorithm , Its position accuracy is improved 1 More than times . Next we'll be on the cell phone 、 The computational power consumption of the optimization algorithm on the vehicle terminal , And in the future, high-precision navigation for all scenes 、 Intelligent driving application landing .

Four 、 Summary and prospect

Positioning technology has a long history . in fact , If we go back 20 or 30 years ago or even earlier , At that time, a professional high-precision positioning technology for surveying and mapping was produced , So positioning accuracy itself is not a problem . But today, we should improve the precise positioning under the background of profound changes in people's travel modes , The problem we're facing is how to build affordable users , Technology and application that can really provide convenience for travel .

therefore , In the future, high-precision positioning needs to expand the scene application first , From outside to inside , From driving to walking , Finally, the whole scene coverage is achieved . According to the characteristics of scene application , Clear positioning for high precision in the accuracy of 、 reliability 、 Cost and other dimensions of demand . Fully integrated with the current rapid development of sensing 、 Communications 、 Computing and other fields of Technology , Design the best solution . Possible research and development directions include :

  • Low cost, high precision GNSS technology , Such as PPP-RTK Technology, etc. ; 

  • Based on the latest communication technology ( Such as 5G) High precision positioning ; 

  • Based on the latest perception technology ( Low cost Lidar) High precision positioning ; 

  • Deeper integration of various positioning technologies ( Such as IMU、 Visual aids RTK Solution ). 

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