CN113532435A - Car machine underground parking lot AR navigation method - Google Patents
Car machine underground parking lot AR navigation method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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Abstract
The invention belongs to the technical field of car machine navigation, and discloses an AR navigation method for an underground car machine parking lot, which comprises the following steps: collecting parking lot data, and establishing a parking lot map according to the parking lot data, wherein the parking lot data comprises road network nodes, uphill and downhill slopes, left and right turns, parking space positions and parking space numbers; planning a travel path according to a vehicle starting point, a vehicle terminal point to be driven in and a road network node in a parking lot map; calculating the relative displacement of the vehicle through a dead reckoning algorithm based on the CAN line real-time data of the vehicle, recording the current moving track point of the vehicle, and obtaining the real-time positioning result of the vehicle; correcting the implementation positioning result of the vehicle; and according to the corrected positioning result, overlaying the navigation information to the acquired video in front of the vehicle to form an AR navigation map. The invention realizes the continuous positioning and navigation in the underground parking lot; the low-cost and light-weight positioning navigation is realized by utilizing the vehicle sensor and the map collected in advance.
Description
Technical Field
The invention belongs to the technical field of car machine navigation, and particularly relates to an AR navigation method for an underground car machine parking lot.
Background
With the rapid development of economy and the rapid advance of urbanization, more and more families have private cars, and the demand for large underground parking lots is increased. However, large underground parking lots often have the problems of large scale, complex terrain, road staggering, difficulty in finding roads and the like, and the user experience and the use efficiency are seriously influenced, so that the navigation system in the underground parking lots is more and more emphasized.
However, because the GPS signal in the underground parking lot is shielded, the automobile cannot be directly positioned, and the satellite positioning and navigation system commonly used outdoors fails. The current indoor commonly used positioning modes comprise WIFI hotspot positioning, Bluetooth beacon positioning, infrared positioning, an ultrasonic technology, an ultra-wideband technology, inertial navigation, visual positioning and the like, but most of the technologies depend on large-scale hardware deployment, data acquisition updating, regular maintenance and the like, and all the technologies have various limitations, such as WIFI is easily interfered by the environment, the Bluetooth propagation distance is short, the stability is poor, infrared and UWB are expensive in large-range coverage, the inertial navigation has accumulated errors and cannot be positioned globally, the visual positioning has large operation limitation, the use power consumption is high, and therefore how to realize stable positioning navigation of automobiles in underground parking lots is a problem which generally exists at present.
Modern automobiles are generally provided with all around-looking and front-looking cameras, so that the current position of the automobile CAN be provided in an auxiliary manner when a program is started, and CAN data such as wheel speed, wheel pulse, steering wheel angle and the like, which are equipped in the automobile, CAN be used for dead reckoning in a short distance and CAN also be directly used for predicting driving behaviors. The system for collecting and managing the data of the parking lot is rich, and can provide information such as road network nodes, parking positions and numbers in the parking lot.
The AR navigation provides more realistic navigation information for the driver by superimposing real-time images of the real scene with the navigation information. However, currently, due to the lack of map and location capability of the parking lot, there is no AR navigation system available on the market.
Disclosure of Invention
The invention aims to provide an AR navigation method for an underground parking lot of a vehicle machine, which aims to solve the existing problems.
In order to achieve the purpose, the invention provides the following technical scheme: an AR navigation method for an underground parking lot of a vehicle machine comprises the following steps:
planning a travel path according to a vehicle starting point, a vehicle terminal point to be driven in and a road network node in a parking lot map;
calculating the relative displacement of the vehicle through a dead reckoning algorithm based on the CAN line real-time data of the vehicle, recording the current moving track point of the vehicle, and obtaining the real-time positioning result of the vehicle;
correcting the implementation positioning result of the vehicle;
and according to the corrected positioning result, overlaying the navigation information to the acquired video in front of the vehicle to form an AR navigation map.
As an AR navigation method for an in-vehicle/underground parking lot according to the present invention, it is preferable that before planning a travel path according to a vehicle start point, a destination to which a vehicle is to enter, and a road network node in a parking lot map, the AR navigation method includes:
collecting parking lot data, and establishing a parking lot map according to the parking lot data, wherein the parking lot data comprises road network nodes, uphill and downhill slopes, left and right turns, parking space positions and parking space numbers.
Preferably, the method for AR navigation of an underground car parking lot according to the present invention, wherein the step of correcting the positioning result of the vehicle specifically includes:
and interpolating road network nodes in the parking lot map, registering the current moving track point with the historical moving track point, and correcting the positioning result of the vehicle in a small range.
As an AR navigation method for an in-vehicle/underground parking lot of the present invention, preferably, the step of interpolating a road network node in the parking lot map in S310, registering the current moving track point with a historical moving track point, and correcting the positioning result of the vehicle in a small scale includes:
and transversely comparing the CAN line real-time data, predicting driving behaviors, correcting the deviation of road network nodes near the positioning result in a matching map, and further correcting the positioning result.
Preferably, the CAN line real-time data comprises wheel speed, wheel pulse and steering wheel rotation angle.
Preferably, as the AR navigation method for the car underground parking lot according to the present invention, the driving behaviors include straight traveling, ascending/descending, turning left/right, ending left/right turning, and car-meeting avoidance.
Preferably, the navigation information includes a front track and speed limit information.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes the continuous positioning and navigation in the underground parking lot; the low-cost and light-weight positioning navigation is realized by utilizing the vehicle sensor and the map collected in advance.
Drawings
FIGS. 1-3 are flow diagrams of the present invention;
fig. 4 is a flow chart of another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides the following technical solutions: an AR navigation method for an underground parking lot of a vehicle machine comprises the following steps:
s100, planning a traveling path according to a vehicle starting point, a vehicle destination point and road network nodes in a parking lot map;
s200, calculating the relative displacement of the vehicle through a dead reckoning algorithm based on the CAN line real-time data of the vehicle, recording the current moving track point of the vehicle, and obtaining the real-time positioning result of the vehicle;
s300, correcting the implementation positioning result of the vehicle;
and S400, according to the corrected positioning result, overlaying the navigation information to the acquired video in front of the vehicle to form an AR navigation map.
In the embodiment, the forward-looking camera is used for identifying the entrance and exit channels or gate machines of the parking lot for positioning, the driver can directly input any parking space number near the starting point to realize global positioning, and the driving path is quickly planned by combining the terminal selected by the driver and the map road network node.
Specifically, before the step S100 of planning the travel path according to the vehicle starting point, the destination to be driven in by the vehicle, and the road network node in the parking lot map, the method includes the steps of:
s10, collecting parking lot data, and establishing a parking lot map according to the parking lot data, wherein the parking lot data comprises road network nodes, ascending and descending slopes, left and right turns, parking space positions and parking space numbers.
Specifically, the step S300 of correcting the positioning result of the vehicle specifically includes the steps of:
s310, interpolation is carried out on road network nodes in the parking lot map, the current moving track point and the historical moving track point are registered, and the positioning result of the vehicle is corrected in a small range.
Specifically, in the step S310, interpolating the road network nodes in the parking lot map, registering the current moving track point with the historical moving track point, and correcting the positioning result of the vehicle by a small margin, the method includes the steps of:
s320, transversely comparing the CAN line real-time data, predicting driving behaviors, correcting the deviation of road network nodes near the positioning result in the matching map, and further correcting the positioning result.
Specifically, the CAN line real-time data comprises wheel speed, wheel pulse and steering wheel angle.
Specifically, the driving behaviors include straight running, ascending/descending, about-to-turn left/right, ending left/right turn, and vehicle avoidance.
Specifically, the navigation information includes a front track and speed limit information.
The invention utilizes the vehicle camera to identify the markers of the entrance and exit, the gate and the like of the parking lot or directly inputs the number of any nearby parking place by the driver to quickly realize global positioning; carrying out dead reckoning by using the CAN data of the vehicle, and matching the dead reckoning with the known parking lot road network information to realize real-time positioning and navigation; directly predicting driving behaviors by using CAN data, and correcting and positioning by combining with parking lot road network node information; the AR navigation capability in the parking lot is provided, and navigation information is superposed to a video shot by a camera in front of the current vehicle.
As shown in fig. 4, another embodiment of the present invention comprises the steps of:
s1 trains a driving behavior prediction model based on CAN data:
and training a driving behavior prediction model by utilizing deep learning based on the CAN data of the vehicle.
S2, building a parking lot map:
collecting data of the parking lot, and building a parking lot map in advance, wherein the parking lot map comprises information such as road network nodes, uphill and downhill slopes, left and right turning, parking space positions and numbers.
S3 global positioning module:
the front-view camera is used for identifying the entrance and exit channels or gate machines of the parking lot for positioning, the driver can directly input the number of any parking space near the starting point to realize global positioning, and the driving path is quickly planned by combining the terminal selected by the driver and the map network nodes.
S4 dead reckoning based on CAN data:
based on data such as wheel speed, wheel pulse, steering wheel corner and the like provided by a CAN line of the vehicle in real time, the relative displacement is calculated through a dead reckoning algorithm, so that a real-time positioning result is obtained, and track points are recorded.
S5, correcting and positioning by combining parking lot road network information:
and (4) interpolating the nodes of the parking lot road network, registering the nodes with the historical moving track points, and correcting the positioning result in a small scale.
S6 predicting behaviors, comparing road network nodes, correcting and positioning:
and transversely comparing the CAN data of the vehicles, directly predicting driving behaviors, such as straight driving, ascending/descending, turning left/right, vehicle crossing avoidance and the like, correcting the deviation by matching road network node information near the positioning result in the map, and further correcting the positioning result.
And S7, according to the positioning, overlaying the navigation information to the real-time video provided by the vehicle-mounted front camera. The navigation information comprises the track in front, the speed limit and other common information.
Yet another embodiment of the present invention comprises the steps of:
s01, a parking lot map is established, and the key points comprise road, node and parking space number information.
S02, starting program at entrance of parking lot, waiting for vehicle to identify entrance/exit passage or gate to locate its position.
And S03, selecting the end position and planning the travel path.
S04, based on the real-time CAN data, the displacement is calculated, the road node information is matched, and continuous positioning and navigation in the parking lot are achieved.
S05, the navigation information is superposed on the vehicle-mounted camera and displayed on the central control screen.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. An AR navigation method for an underground parking lot of a vehicle machine is characterized by comprising the following steps:
planning a travel path according to a vehicle starting point, a vehicle terminal point to be driven in and a road network node in a parking lot map;
calculating the relative displacement of the vehicle through a dead reckoning algorithm based on the CAN line real-time data of the vehicle, recording the current moving track point of the vehicle, and obtaining the real-time positioning result of the vehicle;
correcting the implementation positioning result of the vehicle;
and according to the corrected positioning result, overlaying the navigation information to the acquired video in front of the vehicle to form an AR navigation map.
2. The AR navigation method for the vehicle-mounted underground parking lot according to claim 1, wherein the vehicle comprises the following steps before planning a travel path according to a vehicle starting point, a vehicle terminal point to be driven in and a road network node in a parking lot map:
collecting parking lot data, and establishing a parking lot map according to the parking lot data, wherein the parking lot data comprises road network nodes, uphill and downhill slopes, left and right turns, parking space positions and parking space numbers.
3. The AR navigation method for the car underground parking lot according to claim 1, wherein the step of correcting the positioning result of the vehicle specifically comprises the steps of:
and interpolating road network nodes in the parking lot map, registering the current moving track point with the historical moving track point, and correcting the positioning result of the vehicle in a small range.
4. The AR navigation method for the vehicle-mounted underground parking lot according to claim 3, wherein the method comprises the following steps of interpolating road network nodes in the parking lot map, registering the current moving track point with the historical moving track point, and slightly correcting the positioning result of the vehicle:
and transversely comparing the CAN line real-time data, predicting driving behaviors, correcting the deviation of road network nodes near the positioning result in a matching map, and further correcting the positioning result.
5. The AR navigation method for the car underground parking lot of the car machine according to claim 1, wherein the CAN line real-time data comprises wheel speed, wheel pulse and steering wheel turning angle.
6. The AR navigation method for the vehicle underground parking lot according to claim 4, wherein the driving behaviors comprise straight traveling, ascending/descending, about-to-go left/right turning, left/right turning ending and vehicle avoidance.
7. The AR navigation method for the vehicle-mounted underground parking lot according to claim 1, wherein the navigation information comprises a front track and speed limit information.
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| CN202110683028.XA CN113532435A (en) | 2021-06-18 | 2021-06-18 | Car machine underground parking lot AR navigation method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115497330A (en) * | 2022-09-28 | 2022-12-20 | 中国第一汽车股份有限公司 | Parking space path finding method and system based on augmented reality and vehicle thereof |
| CN117191072A (en) * | 2023-11-07 | 2023-12-08 | 山东高速信息集团有限公司 | Highway road live-action navigation system |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101102408B1 (en) * | 2010-10-06 | 2012-01-05 | 주식회사 인스프리트 | Parking guide system and method using augmented reality |
| CN103105174A (en) * | 2013-01-29 | 2013-05-15 | 四川长虹佳华信息产品有限责任公司 | AR (augmented reality)-based vehicle-mounted live-action safe navigation method |
| CN207319476U (en) * | 2017-07-18 | 2018-05-04 | 俞正义 | A kind of intelligent reverse vehicle searching system based on AR real scene navigations |
| CN108109423A (en) * | 2017-12-26 | 2018-06-01 | 武汉创驰蓝天信息科技有限公司 | Underground parking intelligent navigation method and system based on WiFi indoor positionings |
| US20180327028A1 (en) * | 2015-12-08 | 2018-11-15 | Panasonic Intellectual Property Management Co. Ltd. | Parking assistance device, parking assistance method, and parking assistance program |
| CN109070746A (en) * | 2016-04-15 | 2018-12-21 | 三菱电机株式会社 | Parking aid |
| CN109212574A (en) * | 2018-11-01 | 2019-01-15 | 上海安吉四维信息技术有限公司 | Mobile phone and vehicle device interconnection positioning system, method, navigation system and automobile |
| CN109766757A (en) * | 2018-12-11 | 2019-05-17 | 惠州市德赛西威汽车电子股份有限公司 | A kind of parking position high-precision locating method and system merging vehicle and visual information |
| CN110517533A (en) * | 2019-09-29 | 2019-11-29 | 武汉中海庭数据技术有限公司 | A kind of autonomous parking method and system |
| KR20200025421A (en) * | 2018-08-30 | 2020-03-10 | 이준서 | Augmented Reality Based Parking Guidance System in Indoor Parking Lot |
| CN111366161A (en) * | 2020-05-29 | 2020-07-03 | 北京晶众智慧交通科技股份有限公司 | Vehicle positioning method and electronic equipment |
| CN212220188U (en) * | 2020-05-08 | 2020-12-25 | 上海追势科技有限公司 | Underground parking garage fuses positioning system |
| CN112229418A (en) * | 2020-12-11 | 2021-01-15 | 北京晶众智慧交通科技股份有限公司 | Vehicle positioning system based on OBD box |
| CN215341428U (en) * | 2021-06-18 | 2021-12-28 | 上海追势科技有限公司 | Car machine underground parking garage AR navigation |
-
2021
- 2021-06-18 CN CN202110683028.XA patent/CN113532435A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101102408B1 (en) * | 2010-10-06 | 2012-01-05 | 주식회사 인스프리트 | Parking guide system and method using augmented reality |
| CN103105174A (en) * | 2013-01-29 | 2013-05-15 | 四川长虹佳华信息产品有限责任公司 | AR (augmented reality)-based vehicle-mounted live-action safe navigation method |
| US20180327028A1 (en) * | 2015-12-08 | 2018-11-15 | Panasonic Intellectual Property Management Co. Ltd. | Parking assistance device, parking assistance method, and parking assistance program |
| CN109070746A (en) * | 2016-04-15 | 2018-12-21 | 三菱电机株式会社 | Parking aid |
| CN207319476U (en) * | 2017-07-18 | 2018-05-04 | 俞正义 | A kind of intelligent reverse vehicle searching system based on AR real scene navigations |
| CN108109423A (en) * | 2017-12-26 | 2018-06-01 | 武汉创驰蓝天信息科技有限公司 | Underground parking intelligent navigation method and system based on WiFi indoor positionings |
| KR20200025421A (en) * | 2018-08-30 | 2020-03-10 | 이준서 | Augmented Reality Based Parking Guidance System in Indoor Parking Lot |
| CN109212574A (en) * | 2018-11-01 | 2019-01-15 | 上海安吉四维信息技术有限公司 | Mobile phone and vehicle device interconnection positioning system, method, navigation system and automobile |
| CN109766757A (en) * | 2018-12-11 | 2019-05-17 | 惠州市德赛西威汽车电子股份有限公司 | A kind of parking position high-precision locating method and system merging vehicle and visual information |
| CN110517533A (en) * | 2019-09-29 | 2019-11-29 | 武汉中海庭数据技术有限公司 | A kind of autonomous parking method and system |
| CN212220188U (en) * | 2020-05-08 | 2020-12-25 | 上海追势科技有限公司 | Underground parking garage fuses positioning system |
| CN111366161A (en) * | 2020-05-29 | 2020-07-03 | 北京晶众智慧交通科技股份有限公司 | Vehicle positioning method and electronic equipment |
| CN112229418A (en) * | 2020-12-11 | 2021-01-15 | 北京晶众智慧交通科技股份有限公司 | Vehicle positioning system based on OBD box |
| CN215341428U (en) * | 2021-06-18 | 2021-12-28 | 上海追势科技有限公司 | Car machine underground parking garage AR navigation |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115497330A (en) * | 2022-09-28 | 2022-12-20 | 中国第一汽车股份有限公司 | Parking space path finding method and system based on augmented reality and vehicle thereof |
| CN117191072A (en) * | 2023-11-07 | 2023-12-08 | 山东高速信息集团有限公司 | Highway road live-action navigation system |
| CN117191072B (en) * | 2023-11-07 | 2024-01-26 | 山东高速信息集团有限公司 | Highway road live-action navigation system |
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