CN106767853B - Unmanned vehicle high-precision positioning method based on multi-information fusion - Google Patents
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Abstract
The invention relates to a high-precision positioning method of an unmanned vehicle based on multi-information fusion, which can be applied to environment perception and intelligent decision of the unmanned vehicle. The invention realizes high-precision real-time positioning by using the cooperation of the off-line map and the on-line sensing information. The off-line map records road traffic information of the driving area of the unmanned vehicle. The on-line perception information includes lane lines and road boundaries. When an unmanned vehicle runs in a map area, the approximate position of the vehicle is determined according to positioning information given by an inertial integrated navigation system, a local map near the position is obtained, a lane line in front of the vehicle and road boundaries on two sides of the vehicle are detected through a vehicle-mounted sensor, the relative positions of the vehicle, the lane line and the road boundaries are determined, the position of the vehicle in the map is compared, deviation is calculated, positioning errors are corrected, and high-precision positioning is achieved.
Description
Technical Field
The invention belongs to the technical field of unmanned vehicles, and particularly relates to a high-precision positioning method of an unmanned vehicle based on multi-information fusion.
Background
An unmanned vehicle is an intelligent vehicle which can automatically complete driving tasks. The vehicle-mounted sensor senses the road environment, and a proper driving strategy is adopted to control the vehicle to safely and reliably reach the destination. The unmanned vehicle is a product of high development of computer science, mode recognition and intelligent control technology, and has wide application prospect in the fields of national defense and national economy.
High precision positioning is a necessary condition for achieving unmanned driving. The unmanned vehicle can accurately judge the position of the unmanned vehicle by utilizing high-precision positioning and matching with a high-precision map, is familiar with the road traffic environment near the vehicle and reduces the requirement of a sensing system on environment detection. The high-precision positioning can help the decision-making system to plan the driving path in real time, select a proper lane, process various traffic conditions, effectively improve the driving quality and enhance the safety and intelligence of driving.
Conventional positioning usually consists of a satellite positioning system (GPS, beidou, etc.) plus an Inertial Navigation System (INS). Satellite positioning signals are easily interfered under the condition that high-rise trees shield the satellite positioning signals, and the signal quality is reduced. If the INS system alone is used to output positioning information, the error will increase rapidly over time.
Patent publication No. CN104089619A (application No. CN201410202876.4) provides a GPS navigation map exact matching system for an unmanned vehicle and an operation method thereof. The method comprises a positioning module, a map module and a matching module, wherein the positioning module acquires real-time positioning information and path track information of the vehicle, the map module makes the information into a map analysis module and a map loading module of a KML text map, and the matching module matches the optimal route for the vehicle in real time by using the information of the positioning module and the map module in the driving process. The KML map is matched with the longitude and latitude measured by the GPS in real time, local road environment characteristics are not utilized, and the KML map has a limited application range and insufficient positioning accuracy due to single information source and deviation of the map and the GPS information.
The positioning scheme of multi-sensor information fusion is adopted, various sensors can be combined together, an unmanned vehicle firstly receives satellite and inertial navigation positioning signals to realize coarse positioning, and then an environment sensing system is matched to acquire data by using sensors such as a vehicle-mounted laser radar and a camera to construct a two-dimensional or three-dimensional map of a scene, so that environment characteristics are extracted, and high-precision positioning under a local environment is obtained by means of map characteristic matching. The detection ranges of different sensors are different, and the adaptive environments are different. And a plurality of information sources are provided, so that the detection range can be enlarged, the information redundancy is increased, and the robustness and the reliability of the system are effectively enhanced.
Disclosure of Invention
The technical problem of the invention is solved: the invention overcomes the defects of the prior art and provides a high-precision positioning method of the unmanned vehicle based on multi-information fusion.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a high-precision positioning method of an unmanned vehicle based on multi-information fusion, which fuses information provided by a map module, a camera processing module and a radar processing module, and enables the position of the vehicle in the map to be matched with the real position of the vehicle in the environment through lane line correction and boundary correction. Wherein:
the map module is used for drawing a local map which takes the vehicle as the center and is in a certain range according to the real-time positioning information, and the local map is used by the camera processing module and the radar processing module;
the camera processing module is used for acquiring original road surface data in the driving process of the vehicle by using a camera, extracting lane lines and using the lane lines for map matching and correction;
and the radar processing module is used for collecting the surrounding environment information of the vehicle by using a laser radar, and extracting the road boundary for map matching and correction.
The camera processing module evaluates the availability of the detected lane line before map matching and correction, and the evaluation indexes comprise: a lane angle _ camera, a nose angle _ car, and a lane width difference line _ width,
angle _ camera: representing the included angle between the lane line detected in real time and the lane line in the local map;
angle _ car: representing the included angle between the vehicle head direction detected in real time and the lane line in the local map;
line _ width: and the width difference between the lane line detected in real time and the lane line in the local map is shown.
Wherein, threshold vectors are set for the lane line availability evaluation indexes
If any index exceeds the threshold value, the lane line has no availability and cannot be used for map correction.
The radar processing module carries out usability evaluation on the detected road boundary before map matching and correction, and the evaluation indexes comprise: angle angleDT, width widthDT, and line type leftDDT, rightDDT,
angleDT: a deflection angle representing a road in the local map;
width DT: represents the average width of the road;
leftDDT, rightDDT: the shape deviation of the left and right boundaries is respectively represented, and the linear shape of the boundaries adopts quadratic curve fitting.
Wherein, according to the failure times of the lane line detection and the distance between the vehicle and the actual detection boundary, the usability evaluation of the boundary is divided into three grades: common, strict and unlimited, respectively corresponding to different threshold vectors
Wherein, calculate the detection distance, namely the distance of vehicle and lane line or boundary through the assessment, find the display distance correspondingly, namely the distance of vehicle and lane line or boundary in the map, the detection distance is not always equal to display distance, the difference between them is the positioning deviation of the local map that needs to be corrected, the deviation correction formula:
Adjust_x+＝offset*cos(rad)；
Adjust_y+＝offset*sin(rad)； (1)
wherein:
adjust _ x: indicating deviation of the map in the true east direction;
adjust _ y: representing the deviation of the map in the true north direction;
offset: indicating lane line or boundary deviation;
rad: representing a map yaw angle;
cos: a trigonometric function cosine function;
sin: a trigonometric function sine function;
the correction results of Adjust _ x and Adjust _ y are permanently retained for use in the next drawing of the local map.
Compared with the prior art, the invention has the advantages that:
the main problems existing in the prior art are that the source of the positioning information is single, the stability and the reliability of the information are insufficient, the information is easily influenced by the environment, and the positioning requirement of the driveway level of the unmanned vehicle cannot be met. The invention has the innovativeness that a plurality of devices are used as data sources, the method is suitable for different environments, various road information is extracted and fused, a map matching correction algorithm is designed according to detection conditions in a grading manner, and the target of high-precision positioning is realized.
(1) The invention utilizes various devices including a combined positioning system, a high-precision map, a laser radar, a camera and the like as data sources, combines information provided by multiple data sources, can quickly provide lane-level high-precision positioning, and has wide application range.
(2) According to the invention, different deviation correction threshold values are respectively designed according to the detection condition of the road information, so that the high-precision positioning result output can be continuously and stably provided under the condition that the detection of the lane line or the boundary part is invalid, and the robustness and the reliability of the system are improved.
(3) The multi-module information fusion method designed by the invention is independent and mutually connected. Each module is responsible for relatively independent task respectively, only carries out mutual communication when necessary, has improved the functioning speed of system, has guaranteed unmanned vehicle to the real-time nature requirement of high accuracy location.
Drawings
FIG. 1 is a flow chart of a high-precision positioning method of an unmanned vehicle based on multi-information fusion according to the invention;
FIG. 2 is a flow chart of a lane line correction algorithm;
FIG. 3 is a flow chart of a boundary correction algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to specific implementation steps and accompanying drawings. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
As shown in FIG. 1, the high-precision positioning method based on multi-information fusion of the invention comprises the following steps:
step 1, obtaining information from a plurality of data sources. The method comprises the following steps:
1) and the map module is used for extracting a local map of the area where the vehicle is located by combining the high-precision map and the combined positioning system. The map is represented in grid graphs, each grid representing a plot of size 20cm by 20 cm. The local map displays vehicle position and road information including road attribute information such as road width, road length, road morphology, lane width, number of lanes, lane type, etc.
2) And the camera and radar module is used for acquiring road traffic environment information in real time based on the vehicle-mounted camera and the radar equipment and extracting lane lines and road boundaries. Lane line and boundary detection uses algorithms known in the art.
And 2, carrying out usability evaluation on the obtained lane lines and road boundaries.
And 3, after the availability evaluation is passed, calculating the distance between the actually detected lane line or boundary and the vehicle by contrasting with the local map generated by the map module, comparing with the result displayed by the local map, obtaining the positioning deviation, and correcting the positioning deviation.
And 4, reserving a correction result for drawing a local map and correcting the local map at the next time.
For step 1, the data sources include:
the high-precision map is used for extracting road attribute information such as the width, the length and the form of a road, the number, the width and the type of lanes and road surface identification information such as a white solid line, a white dotted line, a sidewalk, a road isolation belt, a straight arrow and a left-turn arrow in each road in a driving target area of the unmanned vehicle. The embodiment adopts a user-defined format to manufacture a high-precision map;
and the laser radar is used for detecting the road boundary. In the embodiment, HDL-64E high-precision laser radar produced by Velodyne is adopted to scan the road environment in real time at 360 degrees, a three-dimensional model is constructed, and then road boundary information is extracted from the three-dimensional model;
and the camera is used for detecting the lane line. In the embodiment, a DFK 23G274 industrial camera produced by Mimex is adopted, the resolution is 640 x 480, a lane line detection algorithm is effectively supported, and the accuracy and the robustness of a detection result are ensured;
and the combined positioning system is used for providing satellite positioning information of the vehicle at a certain moment. The integrated navigation positioning system SPAN-CPT produced by NovAtel company is adopted in the embodiment, and the integrated navigation positioning system is a tightly coupled system integrating GPS and INS, can continuously and stably output positioning information and supports the creation of local maps.
For step 2, lane line assessment and road boundary assessment are included:
lane line assessment, including three indicators: lane angle _ camera, vehicle head angle _ car, and lane width difference line _ width. The angle _ camera represents an included angle between a lane line detected in real time and a lane line in the local map, the angle _ car represents an included angle between a vehicle head direction detected in real time and the lane line in the local map, and the line _ width represents a width difference between the lane line detected in real time and the lane line in the local map.
Setting threshold vectors for lane line availability assessment indicators
If any index exceeds the threshold value, and the angle _ camera is greater than 5, the lane line has no availability and cannot be used for map correction.
The road boundary adopts quadratic curve fitting, and the usability evaluation indexes comprise angle angleDT, width widthDT, linear leftDDT and rightDDT. angleDT represents the yaw angle of the road in the local map, widthDT represents the average width of the road, and leftDDT and rightDDT represent the shape deviation of the left and right boundaries, respectively.
According to the number of times of failure of lane line detection and the distance between the vehicle and the actual detection boundary, the usability evaluation of the boundary is divided into three levels: ordinary, strict and unlimited, respectively corresponding to different thresholds. If the camera processing module does not detect a lane line for 10 consecutive cycles, a strict evaluation is performed, a threshold vector
Otherwise, performing a normal evaluation, threshold vectorUnder strict evaluation conditions, if the distance from the left boundary or the right boundary of the actually detected road to the vehicle is less than 7, unlimited evaluation is performed.
With respect to the step 3 of the method,
the detected distance, i.e. the distance of the vehicle from the lane line or boundary passing the evaluation, is calculated, corresponding to the found display distance, i.e. the distance of the vehicle from the lane line or boundary in the map. The detection distance and the display distance are not necessarily equal, and the difference value of the two distances is the positioning deviation of the local map needing to be corrected. The deviation correction method comprises the following steps: if the lane line deviation is equal to or less than the threshold 10 or the boundary deviation is equal to or more than the threshold y, the correction is performed by the above equation (1). The correction result is permanently retained and used when the local map is drawn next time. The threshold value y has different values according to different evaluation grades. The unlimited evaluation y is 0, the strict evaluation y is 3, and the general evaluation y is 5.
The map module, the camera processing module and the radar processing module are in parallel relation with each other, operate in three threads respectively, and communicate with each other through a public data storage space.
Fig. 2 is a flowchart of a camera processing module lane line correction algorithm, and fig. 3 is a flowchart of a radar processing module boundary correction algorithm.
In a word, the invention relates to a high-precision positioning method of an unmanned vehicle based on multi-information fusion, which can be applied to environment perception and intelligent decision of the unmanned vehicle. The method obtains a local map of a vehicle driving area through satellite positioning, detects road boundaries and lane lines on line by using a laser radar and a camera, evaluates the availability of the obtained boundary information and lane line information, and calculates the distance deviation between an actual detection result and a map display result after determining that the actual detection result can be used. And correcting the deviation to obtain a new local map, sending the corrected local map to a decision system through a network, and reserving a correction result for next correction. The method fully utilizes the advantage of multi-information fusion, meets the requirements of the unmanned vehicle on the real-time performance and the precision of high-precision positioning, has robustness, and can effectively adapt to the change of the environment.
The invention has not been described in detail and is part of the common general knowledge of a person skilled in the art.
The foregoing is a detailed description of the present invention with reference to specific embodiments, but the present invention is not to be considered as limited to the specific embodiments. Numerous modifications and variations may be made thereto by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims (4)
1. A high-precision positioning method for an unmanned vehicle based on multi-information fusion is characterized by comprising the following steps: fusing information provided by the map module, the camera processing module and the radar processing module, and matching the position of the vehicle in the map with the real position of the vehicle in the environment through lane line correction and boundary correction, wherein:
the map module is used for drawing a local map which takes the vehicle as the center and is in a certain range according to the real-time positioning information, and the local map is used by the camera processing module and the radar processing module;
the camera processing module is used for acquiring original road surface data in the driving process of the vehicle by using a camera, extracting lane lines and using the lane lines for map matching and correction;
the radar processing module is used for collecting vehicle surrounding environment information by using a laser radar, extracting road boundaries and matching and correcting a map;
the radar processing module carries out usability evaluation on the detected road boundary before map matching and correction, and the evaluation indexes comprise: angle angleDT, width widthDT, and line type leftDDT, rightDDT,
angleDT: a deflection angle representing a road in the local map;
width DT: represents the average width of the road;
leftDDT, rightDDT: respectively representing the shape deviation of the left boundary and the right boundary, and fitting the linear shape of the boundaries by using a quadratic curve;
according to the number of times of failure of lane line detection and the distance between the vehicle and the actual detection boundary, the usability evaluation of the boundary is divided into three levels: common, strict and unlimited, respectively corresponding to different threshold vectors
2. The unmanned vehicle high-precision positioning method based on multi-information fusion is characterized in that: the camera processing module carries out usability evaluation on the detected lane lines before map matching and correction, and the evaluation indexes comprise: a lane angle _ camera, a nose angle _ car, and a lane width difference line _ width,
angle _ camera: representing the included angle between the lane line detected in real time and the lane line in the local map;
angle _ car: representing the included angle between the vehicle head direction detected in real time and the lane line in the local map;
line _ width: and the width difference between the lane line detected in real time and the lane line in the local map is shown.
3. The unmanned vehicle high-precision positioning method based on multi-information fusion as claimed in claim 2, characterized in that: setting threshold vectors for lane line availability assessment indicators
If any index exceeds the threshold value, the lane line has no availability and cannot be used for map correction.
4. The unmanned vehicle high-precision positioning method based on multi-information fusion is characterized in that: calculating a detection distance, namely the distance between the vehicle and the lane line or the boundary which passes the evaluation, correspondingly finding a display distance, namely the distance between the vehicle and the lane line or the boundary in the map, wherein the detection distance and the display distance are not necessarily equal, the difference value of the two distances is the positioning deviation of the local map which needs to be corrected, and a deviation correction formula is as follows:
Adjust_x+＝offset*cos(rad)；
Adjust_y+＝offset*sin(rad)； (1)
wherein:
adjust _ x: indicating deviation of the map in the true east direction;
adjust _ y: representing the deviation of the map in the true north direction;
offset: indicating lane line or boundary deviation;
rad: representing a map yaw angle;
cos: a trigonometric function cosine function;
sin: a trigonometric function sine function;
the correction results of Adjust _ x and Adjust _ y are permanently retained for use in the next drawing of the local map.
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