EP3724811A1 - Method for creating a feature-based localization map for a vehicle, taking into consideration characteristic structures of objects - Google Patents
Method for creating a feature-based localization map for a vehicle, taking into consideration characteristic structures of objectsInfo
- Publication number
- EP3724811A1 EP3724811A1 EP18814502.3A EP18814502A EP3724811A1 EP 3724811 A1 EP3724811 A1 EP 3724811A1 EP 18814502 A EP18814502 A EP 18814502A EP 3724811 A1 EP3724811 A1 EP 3724811A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- vehicle
- feature
- map
- creating
- data
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000004807 localization Effects 0.000 title claims abstract description 34
- 238000013459 approach Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000003780 insertion Methods 0.000 claims description 2
- 230000037431 insertion Effects 0.000 claims description 2
- 238000005259 measurement Methods 0.000 description 11
- 238000011161 development Methods 0.000 description 5
- 230000018109 developmental process Effects 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 4
- 238000013507 mapping Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000000280 densification Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000012358 sourcing Methods 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
-
- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3859—Differential updating map data
-
- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3863—Structures of map data
- G01C21/3867—Geometry of map features, e.g. shape points, polygons or for simplified maps
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
Definitions
- the invention relates to a method for creating a feature-based localization map for a vehicle.
- the invention further relates to a
- the invention further relates to a computer program product.
- SLAM simultaneous localization and mapping
- an automated measurement vehicle e.g., robot, not shown
- Estimate motion estimates (e.g., by inertial sensors,
- the measuring vehicle determines its surroundings by means of a series of measurements by targeting identified observations of landmarks.
- Each of the landmarks may be observed multiple times from each observation point, so there are more landmark measurements than current landmarks.
- the goal of Graph-SLAM or Full-SLAM is to determine from the measurements of the measuring vehicle the true path of the measuring vehicle which the measuring vehicle has taken through the surroundings or the real positions of the surroundings. This is done by comparing the landmarks to each Measuring point were determined performed. In this process, the true map of the environment is to be determined. This is done by corresponding measurements of the same landmark from different measurement points, which are used to perform a measurement vehicle path estimation and environmental detection simultaneously.
- An object of the present invention is to provide an improved method for creating a feature-based location map for a vehicle.
- the object is achieved according to a first aspect with a method for creating a feature-based localization map for a vehicle, comprising the steps:
- the object is achieved with a device for creating a feature-based localization map for a vehicle, comprising:
- a determination device for determining data of at least one object in the environment of the vehicle
- an identifying device for identifying characteristic structures of the at least one object; a summarizing means for summarizing the characteristic structures into a simplification structure of the object;
- an inserter for inserting the simplification structure into the feature-based location map.
- An advantageous development of the method provides that at least one of: radar sensor, ultrasound sensor, lidar sensor, camera is used to determine the data of the at least one object in the environment of the vehicle. In this way, the method can be realized with a multiplicity of different sensor concepts.
- Operation record the data of at least one object in the environment of the vehicle.
- a further advantageous development of the method provides that prior knowledge is used to identify the characteristic structures of the at least one object. In this way, the identification of the characteristic structures of the at least one object can be performed more efficiently and faster. For example, for this purpose
- a further advantageous development of the method provides that the card is updated at defined intervals, preferably after a defined number of days, more preferably after a defined number of hours. In this way, a high utility of such a trained feature-based location map is supported for many users.
- a further advantageous development of the method provides that data of objects in the environment of vehicles are provided by means of a crowdsourcing-based approach.
- non-dedicated investigation vehicles must capture the data of the objects, but it can ordinary road users to create the proposed feature-based localization map.
- a determination quality is advantageously increased in this way.
- Disclosed method features are analogous to corresponding disclosed device features and vice versa. This means
- Fig. 1 is a schematic representation of an effect of
- FIG. 3 shows basic illustrations of effects of conventional and inventive SLAM-based map generation
- Fig. 4 is a block diagram of a proposed device for
- FIG. 5 shows a basic sequence of a proposed method for creating a feature-based localization map for a vehicle.
- an automated motor vehicle can also be understood synonymously as a partially automated motor vehicle, autonomous motor vehicle and partially autonomous motor vehicle.
- Ultrasonic sensors are known.
- these systems generally also have a radio interface (for example realized via a connectivity unit) for transmitting the measured sensor data to a server.
- a radio interface for example realized via a connectivity unit
- a server for example, a server for transmitting the measured sensor data to a server.
- entire vehicle fleets can map their collective environment using the vehicle sensors by providing their sensor data, e.g. transferred to a server.
- the transmission of such so-called “fleet mapping data” is known.
- the sensor data is collected on the server and a digital map for the relevant road section is generated from the data of several trips and / or vehicles.
- the digital maps determined in this way also called HAD maps, AD maps or HD maps
- HAD maps, AD maps or HD maps are used, inter alia, for locating automatically moving vehicles in the digital map (for example for determining trajectories).
- landmarks are used, which are listed in the digital map with their exact geographical position.
- Typical landmarks are eg lane markings, street signs, guardrails, etc. If an automatically driving vehicle recognizes one or more landmarks with the aid of the vehicle sensor system and can clearly find these landmarks in the digital map, this can result in a very accurate relative position of the vehicle relative to the landmark of the digital map be derived. Density and quality of the landmarks thus significantly affect a quality of localization with respect to the accuracy of the detected position. In reality, there are sections of track that have many and well-usable landmarks, as well as sections that have poor coverage of landmarks, which may result in poor localization quality.
- the mentioned graph-based SLAM algorithms are used for the purpose of mapping and can in principle be divided into two basic steps:
- This step envisages identifying identical features of multiple runs of the same area by comparing recognized landmarks.
- the identified relationships between measurement positions of different journeys are expressed as edges of a graph representation.
- SLAM-generated maps As stated above, one of the key aspects of SLAM-generated maps is that the data from multiple trips along the route be included in the map. Therefore, most Real World objects are represented multiple times in the map after the optimization step, after being observed during each trip that contributes to the map.
- SLAM techniques are used to generate maps from crowdsourced data that allow for precise feature-based localization.
- the feature-based localization provides that a mapped representation of observed objects in the environment of the vehicle is compared with data of a current environmental sensation of the vehicle and compared with them. This results in an estimate of the current one
- a clustering step in SLAM-based map generation generally works well with punctiform targets using well-known algorithms, e.g. Masts for traffic signs.
- FIG. 1 shows in three views a), b), c) a principle of a determination of a balanced radar localization map and a corresponding idealized compression of this map using known methods for clustering punctiform objects. It can be seen in Fig. 1a by means of a radar sensor detected guard rail attachment points of the card. In Fig. 1c is indicated that the detected guard rail attachment points are sufficiently represented by individual points in the compressed representation.
- a key idea of the present invention is to provide representations of feature-based location maps not only using point-like representations of objects, but to provide for describing complex structures in a more complex manner to thereby facilitate alignment of data with the object
- FIG. 2 a shows an exemplary complex object in the form of a symbolized electric power pole, which is represented in FIG. 2 b in a conventional manner by a single point-shaped object.
- Fig. 3a shows probability ranges A1, A2 of a localization associated with a specific representation of the object. Recognizable are a relatively large region A1 of high localization quality and a relatively large region A2 of low localization quality, based on a punctiform representation e.g. an object "power pole" in the feature-based
- Fig. 3b which is shown in the same scale as Fig. 3a, that the two said probability ranges A1, A2 by a Representation of the object "power pole" in the above-mentioned semi-semantic manner with several points are advantageously substantially reduced.
- a more accurate location for the vehicle can be achieved in comparison with the ratios of FIG. 3a by means of the feature-based localization map and detected radar sensor data.
- an autonomous or automated driving function can be maintained longer for an automated vehicle.
- the autonomous or automated driving function is switched off, whereupon the driver at least temporarily manual control of the vehicle
- the proposed method can also be used with other sensors, e.g. a lidar, ultrasonic sensor, or a camera can be performed.
- sensors e.g. a lidar, ultrasonic sensor, or a camera can be performed.
- a location-based map with a crowd-sourcing-based approach such that a large number of vehicles that do not act as dedicated discovery vehicles transmit the captured data to a central location (not shown) that updates the feature-based location map, for example, at a defined high update frequency of a few days or hours.
- the vehicles providing the feature-based data can thus both be data providers for the feature-based localization map and at the same time users of the map currently being created.
- FIG. 4 shows, in principle, a block diagram of a device 100 for creating a feature-based localization map.
- the determination device 110 for determining data by means of an object in the environment of the vehicle.
- the determination device 110 is functionally connected to an identification device 120, which is used for Identifying characteristic structures of the at least one object is provided.
- the identification device 120 is operatively connected to a summarizer 130, which is provided for summarizing the characteristic structures into a simplification structure of the object.
- the summarizer 130 is operatively connected to an inserter 100 for inserting the
- Simplification structure is provided in the feature-based localization map.
- the proposed method can be implemented as a software with program code means for running on an electronic device 100, whereby an easy changeability and adaptability of the method is supported.
- FIG. 5 shows a basic sequence of an embodiment of the invention
- a determination of data of at least one object in the environment of the vehicle is performed.
- a step 210 identification of characteristic structures of the at least one object is performed.
- a summary of the characteristic structures to a simplification structure of the object is performed.
- step 230 an insertion of the simplification structure into the feature-based location map is performed.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Multimedia (AREA)
- Databases & Information Systems (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Electromagnetism (AREA)
- Aviation & Aerospace Engineering (AREA)
- Geometry (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102017222810.5A DE102017222810A1 (en) | 2017-12-14 | 2017-12-14 | Method for creating a feature-based localization map for a vehicle taking into account characteristic structures of objects |
PCT/EP2018/082122 WO2019115192A1 (en) | 2017-12-14 | 2018-11-21 | Method for creating a feature-based localization map for a vehicle, taking into consideration characteristic structures of objects |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3724811A1 true EP3724811A1 (en) | 2020-10-21 |
Family
ID=64604605
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP18814502.3A Pending EP3724811A1 (en) | 2017-12-14 | 2018-11-21 | Method for creating a feature-based localization map for a vehicle, taking into consideration characteristic structures of objects |
Country Status (7)
Country | Link |
---|---|
US (1) | US11442462B2 (en) |
EP (1) | EP3724811A1 (en) |
JP (1) | JP7427587B2 (en) |
KR (1) | KR20200097772A (en) |
CN (1) | CN111480165A (en) |
DE (1) | DE102017222810A1 (en) |
WO (1) | WO2019115192A1 (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102019208384A1 (en) * | 2019-06-07 | 2020-12-10 | Robert Bosch Gmbh | Method for creating a universally applicable feature map |
DE102019208504A1 (en) * | 2019-06-12 | 2020-12-17 | Robert Bosch Gmbh | Position determination based on environmental observations |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102009017731A1 (en) * | 2008-04-30 | 2009-11-05 | Continental Teves Ag & Co. Ohg | Self-learning map based on environmental sensors |
EP2722646B1 (en) | 2011-06-14 | 2021-06-09 | Nissan Motor Co., Ltd. | Distance measurement device and environment map generation apparatus |
EP2774057A4 (en) * | 2011-11-01 | 2016-07-06 | Ericsson Telefon Ab L M | Methods and devices for providing, receiving or managing maps |
DE102013221696A1 (en) * | 2013-10-25 | 2015-04-30 | Robert Bosch Gmbh | Method and device for determining a height profile of a road ahead of a vehicle |
JP6325806B2 (en) | 2013-12-06 | 2018-05-16 | 日立オートモティブシステムズ株式会社 | Vehicle position estimation system |
JP6280409B2 (en) | 2014-03-25 | 2018-02-14 | 株式会社日立製作所 | Self-vehicle position correction method, landmark data update method, in-vehicle device, server, and self-vehicle position data correction system |
US9803985B2 (en) | 2014-12-26 | 2017-10-31 | Here Global B.V. | Selecting feature geometries for localization of a device |
KR102623680B1 (en) | 2015-02-10 | 2024-01-12 | 모빌아이 비젼 테크놀로지스 엘티디. | Sparse map for autonomous vehicle navigation |
JP7066607B2 (en) * | 2015-08-03 | 2022-05-13 | トムトム グローバル コンテント ベスローテン フエンノートシャップ | Methods and systems for generating and using localization criteria data |
DE102015220831A1 (en) * | 2015-10-26 | 2017-04-27 | Robert Bosch Gmbh | A method and apparatus for determining landmarks for a location determination for a moving object and method and apparatus for locating a moving object |
CN105513051B (en) * | 2015-11-26 | 2017-06-20 | 福州华鹰重工机械有限公司 | A kind of Processing Method of Point-clouds and equipment |
DE102016221688A1 (en) * | 2016-11-04 | 2018-05-09 | Robert Bosch Gmbh | Method of locating a vehicle |
US10545029B2 (en) * | 2016-12-30 | 2020-01-28 | DeepMap Inc. | Lane network construction using high definition maps for autonomous vehicles |
US10816346B2 (en) * | 2016-12-30 | 2020-10-27 | DeepMap Inc. | Occupancy map updates based on sensor data collected by autonomous vehicles |
-
2017
- 2017-12-14 DE DE102017222810.5A patent/DE102017222810A1/en active Pending
-
2018
- 2018-11-21 CN CN201880080604.8A patent/CN111480165A/en active Pending
- 2018-11-21 EP EP18814502.3A patent/EP3724811A1/en active Pending
- 2018-11-21 WO PCT/EP2018/082122 patent/WO2019115192A1/en unknown
- 2018-11-21 KR KR1020207020016A patent/KR20200097772A/en unknown
- 2018-11-21 US US16/767,764 patent/US11442462B2/en active Active
- 2018-11-21 JP JP2020532759A patent/JP7427587B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
KR20200097772A (en) | 2020-08-19 |
JP7427587B2 (en) | 2024-02-05 |
US11442462B2 (en) | 2022-09-13 |
DE102017222810A1 (en) | 2019-06-19 |
WO2019115192A1 (en) | 2019-06-20 |
CN111480165A (en) | 2020-07-31 |
JP2021507376A (en) | 2021-02-22 |
US20200348685A1 (en) | 2020-11-05 |
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