TW202115616A - Map data positioning system and method using roadside feature recognition having the advantages of low data volume, low computational complexity, high reliability - Google Patents
Map data positioning system and method using roadside feature recognition having the advantages of low data volume, low computational complexity, high reliability Download PDFInfo
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本發明係有關一種定位技術,特別是指一種應用路側特徵辨識之圖資定位系統及方法。The present invention relates to a positioning technology, in particular to a map data positioning system and method using roadside feature recognition.
自動駕駛車輛(簡稱自駕車)又稱為無人駕駛車輛,不需要人為操作即能感測周圍環境及導航,能以雷達、光學雷達、衛星導航及電腦視覺等技術感測周圍環境。先進的控制系統能將感測資料轉換成適當的導航道路、障礙物及相關標誌。Self-driving vehicles (referred to as self-driving vehicles) are also called unmanned vehicles. They can sense the surrounding environment and navigate without human operation, and can sense the surrounding environment with technologies such as radar, optical radar, satellite navigation, and computer vision. The advanced control system can convert the sensing data into appropriate navigation roads, obstacles and related signs.
常見的自駕車的定位方法包括三角定位法、同步定位與地圖建構(Simultaneous Localization and Mapping, SLAM)定位法、標籤(Tag)定位法及基於指紋的地圖定位法(Fingerprint Based Map)。其中,三角定位法需要量測目標物及三個已知位置參考點的距離,求出以三個參考點為圓心的圓形的交會點,但其缺點是需要三個以上的參考點,且無航向資訊,定位精度較低;同步定位與地圖建構定位法係以光達掃描行駛路徑點雲圖,再以點雲比對手法去估測車輛位置,然而,建立點雲圖相當耗時,且資料量大,每1公里約需150MB的資料量,且在點雲特徵少的環境會無法定位,更需要透過差分全球定位系統(DGPS)與車輛轉向動態模型去修正車輛絕對航向;標籤定位法係利用三角函數的原理,以光學雷達掃描已知點的標籤,再反推車輛位置,例如已知公車站牌的座標為(x, y),車輛與公車站牌的距離為d,夾角為θ,則車輛的位置為(x-dsinθ, y-dcosθ),但此技術同樣需要透過差分全球定位系統(DGPS)與車輛轉向動態模型去修正車輛絕對航向,且標籤佈建不易,容易被路樹、行人或其他障礙物遮蔽;基於指紋的地圖定位法係先由第一輛車以光學雷達掃描行駛路徑點雲圖,第二輛車比對點雲圖以估測車輛位置,但建立點雲圖耗時,雖然資料量較同步定位與地圖建構定位法少,但資料須一格一格的經過編碼計算,運算量較大,且在點雲特徵少的環境下也具有無法定位的問題。Common positioning methods for self-driving cars include triangulation, Simultaneous Localization and Mapping (SLAM) positioning, Tag positioning and Fingerprint Based Map. Among them, the triangulation method needs to measure the distance between the target and three known position reference points, and find the intersection point of the circle with the three reference points as the center, but its disadvantage is that it requires more than three reference points, and There is no heading information, and the positioning accuracy is low; the synchronous positioning and map construction positioning method uses Lidar to scan the point cloud of the driving path, and then use the point cloud to compare the opponent method to estimate the position of the vehicle. However, the establishment of the point cloud is time-consuming and data Large amount of data, about 150MB of data per 1 km, and in an environment with few point cloud features, it will not be able to locate, and it is necessary to correct the absolute heading of the vehicle through the differential global positioning system (DGPS) and the vehicle steering dynamic model; tag positioning method Using the principle of trigonometric function, the optical radar scans the label of the known point, and then the vehicle position is reversed. For example, the coordinates of the bus stop plate are known as (x, y), the distance between the vehicle and the bus stop plate is d, and the angle is θ , The position of the vehicle is (x-dsinθ, y-dcosθ), but this technology also needs to correct the absolute heading of the vehicle through the differential global positioning system (DGPS) and the vehicle steering dynamic model, and the label deployment is not easy, and it is easily affected by road trees. , Pedestrians or other obstacles; the fingerprint-based map positioning method first scans the point cloud of the driving path with the optical radar of the first car, and compares the point cloud with the second car to estimate the position of the vehicle, but the establishment of the point cloud is time-consuming Although the amount of data is less than the synchronous positioning and map construction positioning method, the data must be coded and calculated one by one, which requires a large amount of calculation, and it also has the problem of inability to locate in an environment with few point cloud features.
因此,本發明即提出一種應用路側特徵辨識之圖資定位系統及方法,有效解決上述該等問題,具體架構及其實施方式將詳述於下:Therefore, the present invention proposes a map data positioning system and method using roadside feature recognition to effectively solve the above-mentioned problems. The specific architecture and implementation methods will be described in detail below:
本發明之主要目的在提供一種應用路側特徵辨識之圖資定位系統及方法,其分別從道路上方取得俯視道路的道路影像地圖,及從平面取得行駛環境的點雲圖,利用資訊空間疊套之技術,從該道路影像地圖中快速區分出道路空間及路側空間,並取得特定物體類別之空間資訊,以將不需要的動態物件濾除,保留可做為路側特徵點的靜態物件,建立精度高而資料量小的定位圖資。The main purpose of the present invention is to provide a map data positioning system and method using roadside feature recognition, which respectively obtain a road image map overlooking the road from the top of the road, and a point cloud image of the driving environment from a plane, using the technology of information space overlay , Quickly distinguish the road space and the roadside space from the road image map, and obtain the spatial information of specific object categories to filter out unwanted dynamic objects and retain static objects that can be used as roadside feature points to create high precision and Location map data with small amount of data.
本發明之另一目的在提供一種應用路側特徵辨識之圖資定位系統及方法,其利用空拍機取得道路影像地圖,藉由解析度高的照相機,僅需低成本空拍圖,便可取得高精度的道路地圖。Another object of the present invention is to provide a map data positioning system and method using roadside feature recognition, which uses an aerial camera to obtain a road image map. With a high-resolution camera, only low-cost aerial images can be obtained. High-precision road map.
本發明之再一目的在提供一種應用路側特徵辨識之圖資定位系統及方法,其進一步利用路側特徵點做為參考點以計算移動載具之航向角度,更精確地定位出移動載具的位置。Another object of the present invention is to provide a map data positioning system and method using roadside feature recognition, which further uses roadside feature points as reference points to calculate the heading angle of the mobile vehicle, and more accurately locate the position of the mobile vehicle .
為達上述目的,本發明提供一種應用路側特徵辨識之圖資定位方法,包括:利用至少一第一偵測器俯視道路進行量測,建置一道路影像地圖,該道路影像地圖中包括複數特徵點;將至少一第二偵測器安裝於至少一移動載具上,偵測該移動載具行進時周圍之行駛環境得到一點雲圖,辨識其中是否包含該等特徵點,並將該等特徵點中之至少一動態物件濾除,依據該道路影像地圖、該點雲圖中剩餘之該等特徵點及設定複數路側特徵點之特徵屬性,以建置一定位圖資;將該定位圖資儲存於一移動載具中,當該移動載具行駛時,利用該移動載具中之一圖資定位系統掃描前方道路,並根據該定位圖資判斷出前方之至少二該路側特徵點,並做為參考點計算一移動載具航向角度;以及利用該移動載具航向角度及該至少二參考點,計算該移動載具之位置。To achieve the above object, the present invention provides a map data positioning method using roadside feature recognition, including: using at least one first detector to look down on the road for measurement, and building a road image map, the road image map including multiple features Point; install at least one second detector on at least one mobile vehicle, detect the surrounding driving environment when the mobile vehicle travels to obtain a point cloud image, identify whether it contains these feature points, and compare the feature points At least one dynamic object is filtered out, based on the road image map, the remaining feature points in the point cloud, and the feature attributes of multiple roadside feature points are set to build a positioning map; the positioning map is stored in In a mobile vehicle, when the mobile vehicle is traveling, one of the map data positioning systems in the mobile vehicle is used to scan the road ahead, and at least two roadside feature points ahead are determined based on the positioning map data, and used as The reference point calculates a heading angle of a mobile vehicle; and the heading angle of the mobile vehicle and the at least two reference points are used to calculate the position of the mobile vehicle.
依據本發明之實施例,該定位圖資之建置方法更包括下列步驟:將該道路影像地圖與該點雲圖疊合,以辨識出一道路空間及至少一路側空間;將該等特徵點中之該等動態物件濾除,保留複數靜態物件做為該等路側特徵點;設定該等路側特徵點之該等特徵屬性;以及依據該道路影像地圖及該點雲圖之一疊合圖、該等路側特徵點及該等特徵屬性,建置該定位圖資。According to the embodiment of the present invention, the method for constructing the location map data further includes the following steps: superimposing the road image map with the point cloud map to identify a road space and at least one side space; among the feature points The dynamic objects are filtered out, and a plurality of static objects are retained as the roadside feature points; the feature attributes of the roadside feature points are set; and based on the road image map and one of the superimposed maps of the point cloud, the The roadside feature points and these feature attributes are used to build the location map.
承上,該路側空間係由內而外將人行道、腳踏車專用道及/或騎樓分為第一、第二路側空間。該特徵屬性包括經緯度座標、形狀、大小、高度等。In addition, the roadside space divides the sidewalk, bicycle lane and/or arcade into the first and second roadside spaces from the inside out. The characteristic attributes include latitude and longitude coordinates, shape, size, height, and so on.
依據本發明之實施例,該移動載具於行駛中擷取路側影像,從中辨識出至少一目標物,並藉由該定位圖資中之該等特徵屬性判斷該目標物是否為該路側特徵點。According to an embodiment of the present invention, the mobile vehicle captures a roadside image during driving, identifies at least one target object from it, and determines whether the target object is the roadside feature point by the feature attributes in the positioning map .
本發明另提供一種圖資定位系統,其裝設於一移動載具之一車上系統中,利用上述方法所建置之該定位圖資進行移動載具定位,該圖資定位系統包括:一資料庫,儲存該定位圖資,該定位圖資中包括複數路側特徵點及該等路側特徵點之複數特徵屬性;一路側特徵辨識模組,根據該定位圖資,掃描道路前方並判斷出符合該等特徵屬性之至少二該路側特徵點;一移動載具航向角度估測模組,利用至少二該路側特徵點做為參考點,計算該移動載具之一移動載具航向角度;以及一移動載具位置估算模組,利用該移動載具航向角度及該至少二參考點,計算該移動載具之位置。The present invention also provides a map data positioning system, which is installed in an on-vehicle system of a mobile vehicle, and uses the positioning map data built by the above method to perform mobile vehicle positioning. The map data positioning system includes: The database stores the location map data. The location map data includes multiple roadside feature points and the multiple feature attributes of the roadside feature points; the road side feature recognition module scans the road ahead according to the location map data and judges that it matches At least two of the characteristic attributes of the roadside feature points; a mobile vehicle heading angle estimation module that uses at least two of the roadside feature points as reference points to calculate the heading angle of one of the mobile vehicles; and The mobile vehicle position estimation module uses the heading angle of the mobile vehicle and the at least two reference points to calculate the position of the mobile vehicle.
本發明提供一種應用路側特徵辨識之圖資定位系統及方法,其從空中擷取高精度的道路影像地圖,再疊套至移動載具行駛周圍的點雲圖中,快速定位出道路與路側之區域,並將影像中的動態物件和靜態物件進行分類,刪除動態物件而僅保留靜態物件,不但可大幅降低定位圖資的資料量,且只需二個參考點便可計算出位置,不須採用三角定位法,運算複雜度也大幅降低,應用在自動駕駛的移動載具定位上,精準度可達到公分,相較於一般衛星定位的精準度即使誤差1~2公尺仍在可接受範圍,本發明之圖資定位方法顯然可確保自動駕駛車的精準度及安全性。The present invention provides a map data positioning system and method using roadside feature recognition, which captures a high-precision road image map from the air, and then overlays it on the point cloud map around the moving vehicle to quickly locate the road and the roadside area , And classify the dynamic objects and static objects in the image, delete the dynamic objects and keep only the static objects, not only can greatly reduce the data amount of the positioning map, but also only need two reference points to calculate the position, no need to use The triangulation method greatly reduces the computational complexity. When applied to the positioning of mobile vehicles for autonomous driving, the accuracy can reach centimeters. Compared with the accuracy of general satellite positioning, the accuracy is still within an acceptable range even if the error is 1 to 2 meters. The map data positioning method of the present invention can obviously ensure the accuracy and safety of the autonomous vehicle.
請參考第1圖,其為本發明應用路側特徵辨識之圖資定位方法之流程圖,主要包括四大步驟,步驟S10先建置定位圖資以供移動載具(如自動駕駛車輛)使用;步驟S12係當移動載具實際行駛時,辨識路側特徵點;步驟S14開始進行移動載具位置的修正,估測移動載具航向角度,步驟S16再計算出移動載具的位置。詳細流程詳述如後。Please refer to Figure 1, which is a flowchart of the map data positioning method using roadside feature recognition of the present invention, which mainly includes four major steps. In step S10, the positioning map data is first constructed for use by mobile vehicles (such as autonomous vehicles); Step S12 is to identify the roadside feature points when the mobile vehicle is actually traveling; step S14 starts to correct the position of the mobile vehicle, estimate the heading angle of the mobile vehicle, and then calculate the position of the mobile vehicle in step S16. The detailed process is detailed below.
第2圖為本發明中建置定位圖資之細部流程圖。首先,步驟S102從道路的上方利用至少一第一偵測器俯視道路進行量測,建置一道路影像地圖,此第一偵測器可為裝有影像擷取裝置的飛行器,如空拍機、無人機、遙控飛機等,影像擷取裝置為相機或攝影機,只要在飛行器上安裝具有高解析度的影像擷取裝置便可擷取高精度的影像,因此道路影像地圖中包括複數特徵點,如車輛、行人等動態物件及紅綠燈、站牌、招牌、建築物、交通標誌等靜態物件,其中,動態物件之判斷係先預設道路空間上的車輛及路側空間上的行人及移動物件為動態物件,並將道路影像地圖中的車輛及行人等刪除;步驟S104將至少一第二偵測器安裝於至少一移動載具上,第二偵測器可為光學雷達(Lidar)或攝影機,攝影機可利用立體影像技術產生三維影像,而移動載具可為汽車,在移動載具移動時,第二偵測器偵測該移動載具周圍之行駛環境,針對掃描到的物體表面建立點雲,這些點雲用來表現物體的表面形狀,愈高密度的點雲可以建立更精確的模型,得到有深度的、三維的點雲圖(point cloud),其包含的資訊為物體的幾何資訊,辨識其中是否包含該等特徵點;接著如步驟S106所述,將道路影像地圖與點雲圖利用資訊空間疊套之方式疊合,以辨識分類出道路空間和路側空間,路側空間之定義較為廣泛,可由內而外將人行道、腳踏車專用道及/或騎樓分為第一、第二路側空間;步驟S107中,將特徵點中之動態物件濾除,僅保留靜態物件做為路側特徵點,步驟S108設定複數路側特徵點的特徵屬性,包括路側特徵點的經緯度座標、形狀、大小、高度等,最後,在步驟S109中依據道路影像地圖及點雲圖之疊合圖、圖中剩餘之路側特徵點(靜態物件)及路側特徵點之特徵屬性,以建置一定位圖資。Figure 2 is a detailed flowchart of the establishment of the positioning map in the present invention. First, step S102 uses at least one first detector to look down the road from the top of the road for measurement to build a road image map. The first detector can be an aircraft equipped with an image capture device, such as an aerial camera. , Drones, remote-controlled aircraft, etc., the image capture device is a camera or video camera. As long as a high-resolution image capture device is installed on the aircraft, high-precision images can be captured. Therefore, the road image map includes multiple feature points. Such as dynamic objects such as vehicles and pedestrians and static objects such as traffic lights, stop signs, signs, buildings, and traffic signs. Among them, the judgment of dynamic objects is to presuppose that vehicles on the road space and pedestrians and moving objects on the roadside space are dynamic Objects, and delete vehicles and pedestrians in the road image map; step S104 install at least one second detector on at least one mobile vehicle, the second detector can be an optical radar (Lidar) or a camera, a camera Three-dimensional imaging technology can be used to generate three-dimensional images, and the moving vehicle can be a car. When the moving vehicle is moving, the second detector detects the driving environment around the moving vehicle and creates a point cloud on the scanned object surface. These point clouds are used to express the surface shape of the object. The higher the density of the point cloud, the more accurate the model can be established, and a deep, three-dimensional point cloud (point cloud) can be obtained. The information contained in it is the geometric information of the object, which can be identified. Whether these feature points are included; then, as described in step S106, the road image map and the point cloud map are superimposed using the information space to identify and classify the road space and the roadside space. The roadside space is defined widely and can be defined by In addition, the sidewalks, bicycle lanes and/or arcades are divided into first and second roadside spaces; in step S107, dynamic objects in the feature points are filtered out, and only static objects are retained as roadside feature points, and step S108 sets a plural number The feature attributes of the roadside feature points include the latitude and longitude coordinates, shape, size, height, etc. of the roadside feature points. Finally, in step S109, according to the superimposed map of the road image map and point cloud image, the remaining roadside feature points in the figure (static objects) ) And feature attributes of roadside feature points to build a location map.
當路側空間上沒有任何靜態物件時,代表沒有路側特徵點,則可直接將該路側空間刪除,只剩餘道路空間,如此將更減少定位圖資的資料量。When there is no static object in the roadside space, it means that there is no roadside feature point, and the roadside space can be deleted directly, leaving only road space, which will further reduce the amount of data for positioning maps.
請參考第3A圖至第3D圖,其為本發明中定位圖資建立之流程示意圖。第3A圖為本發明中從上拍攝之高精度道路影像地圖,從上方可看出哪些部份是道路,哪些部份不是道路(如建築物、公園、停車場等),第3B圖為由移動載具偵測描繪之3D點雲圖透過移動載具位置與高精度道路幾何空間資訊,從中可辨識出道路、車輛、行人、建築物、紅綠燈、站牌、招牌、交通標誌等特徵點;將第3B圖之點雲圖疊套至第3A圖之道路影像地圖後,可得到第3C圖之疊合圖,並在疊合圖上分類出道路空間10和至少一路側空間12、14,例如第一路側空間12可為腳踏車專用道,第二路側空間14為人行道,或是第一路側空間12為人行道,第二路側空間14為騎樓和建築物,在製作定位圖資時,由於車輛、行人等動態物件無法做為路側特徵點,因此將其刪除,若第二路側空間14無任何靜態物件時,此路側空間亦可以刪除之。最後建立出之定位圖資如第3D圖所示,在道路空間上之路側特徵點包括紅綠燈,而路側空間的路側特徵點包括建築物、電塔等地標及交通標誌,但此僅為一個實施例,舉凡有特點的、可做為地標或特徵點的物件皆可做為路側特徵點,如便利商店或速食店的招牌、加油站的招牌等。Please refer to FIG. 3A to FIG. 3D, which are schematic diagrams of the process of establishing location map data in the present invention. Figure 3A is a high-precision road image map taken from above in the present invention. From the top, you can see which parts are roads and which parts are not roads (such as buildings, parks, parking lots, etc.). Figure 3B shows movement by reason The 3D point cloud image drawn by vehicle detection can identify roads, vehicles, pedestrians, buildings, traffic lights, stop signs, signboards, traffic signs and other characteristic points through the position of the mobile vehicle and high-precision road geometric spatial information; After the point cloud image of Figure 3B is overlaid on the road image map of Figure 3A, the overlay image of Figure 3C can be obtained, and the
路側特徵點的特徵屬性會依據不同的物件而設置,例如紅路燈的大小、高度和形狀、公車站牌的大小、高度和形狀、店家招牌的大小、高度和形狀等,一一記錄在定位圖資中。The feature attributes of the roadside feature points will be set according to different objects, such as the size, height and shape of the red street light, the size, height and shape of the bus stop sign, the size, height and shape of the store sign, etc., which are recorded in the location map one by one Zizhong.
當建立完成定位圖資後,其會儲存於雲端平台或移動載具的一圖資定位系統中,圖資定位系統並可定期從雲端更新最新的資訊。此圖資定位系統可設於移動載具之一車上系統中,運算後輸出移動載具之位置資訊。如第4圖所示,其為本發明圖資定位系統22之架構圖,包括一資料庫222、一路側特徵辨識模組224、一移動載具航向角度估測模組226及一移動載具位置估算模組228,其中資料庫222儲存定位圖資,定位圖資中包括複數路側特徵點及路側特徵點之複數特徵屬性;移動載具上所設置之環境感測裝置20會掃描前方道路,將掃描結果傳送給路側特徵辨識模組224,路側特徵辨識模組224再根據定位圖資,判斷掃描的影像中是否有符合特徵屬性之特徵點,若有至少二個符合的路側特徵點,則將其做為參考點;移動載具航向角度估測模組226利用參考點計算移動載具之一移動載具航向角度;移動載具位置估算模組228再利用移動載具航向角度及參考點計算出移動載具之位置。When the positioning map data is created, it will be stored in a map data positioning system on the cloud platform or mobile vehicle. The map data positioning system can periodically update the latest information from the cloud. This map data positioning system can be installed in one of the mobile vehicles' on-board systems, and output the position information of the mobile vehicles after calculation. As shown in Figure 4, it is a structural diagram of the map
在第1圖之步驟S12中,當移動載具行駛時辨識路側特徵之細部流程圖如第5圖所示,移動載具上裝設之環境感測裝置20可為相機、攝影機或光學雷達,於行駛中擷取路側影像,並利用車上系統的處理器利用影像辨識處理技術從中辨識出至少一目標物,並藉由定位圖資中之特徵屬性判斷該目標物是否為路側特徵點,其判斷方法包括:於步驟S122中判斷目標物是否符合路側特徵點的尺寸,若是,再接著於步驟S124判斷目標物是否符合路側特徵點的形狀,若是,再接著於步驟S126判斷目標物是否符合路側特徵點的高度,若是,則如步驟S128所述,此目標物符合某一個路側特徵點,例如紅綠燈;反之,若上述的判斷有任一者為否,則代表目標物不屬於任何一種路側特徵點,立即結束判斷,如步驟S129。In step S12 of Figure 1, the detailed flow chart of identifying roadside features when the mobile vehicle is traveling is shown in Figure 5. The
由於當移動載具之車頭方向與道路並非平行時,下一秒移動載具的位置會與直線前進的位置相距甚遠,因此本發明中為了精準定位,如第1圖之步驟S14所述更增加了計算移動載具航向角度之技術,而第6圖為本發明中計算移動載具航向角度及位置之示意圖。當移動載具行駛時,其車上系統的處理器根據定位圖資判斷出前方之至少二路側特徵點,並做為參考點計算一移動載具航向角度。假設移動載具的座標為(xv ,yv ),路側特徵點之座標為(x1 ,y1 ),則 其中,,。 同理,給予另一個路側特徵點的座標為(x0 ,y0 ),則移動載具的座標計算如下: 由於且,綜合上式後可描述為,其中,,且 因此可得到。When the direction of the head of the moving vehicle is not parallel to the road, the position of the moving vehicle in the next second will be far away from the straight forward position. Therefore, for precise positioning in the present invention, it is added as described in step S14 in Figure 1 The technology of calculating the heading angle of the mobile vehicle is described, and Figure 6 is a schematic diagram of calculating the heading angle and position of the mobile vehicle in the present invention. When the mobile vehicle is driving, the processor of the on-board system judges at least two road side feature points ahead according to the positioning map data, and uses it as a reference point to calculate a heading angle of the mobile vehicle. Assuming that the coordinates of the mobile vehicle are (x v , y v ) and the coordinates of the roadside feature points are (x 1 , y 1 ), then among them, , . In the same way, the coordinates given to another roadside feature point are (x 0 ,y 0 ), then the coordinates of the mobile vehicle are calculated as follows: due to And , Can be described as ,among them , And So you can get .
上述且的等式後會先解出,便可得到車頭與直線前進的夾角,即為移動載具航向角度。藉由此計算方法,只需二個路側特徵點做為參考點便可,無須如三角定位法需得到三個參考點才能計算移動載具位置。Above And Will be solved first after the equation of , The angle between the front of the vehicle and the straight forward movement is obtained, which is the heading angle of the mobile vehicle. With this calculation method, only two roadside feature points are needed as reference points, and there is no need to obtain three reference points as in the triangulation method to calculate the position of the mobile vehicle.
接著進行第1圖步驟S16所述之計算移動載具位置。根據上述至少兩個參考點所推估出的至少兩個移動載具,計算移動載具之位置如下式:, Then proceed to the calculation of the position of the mobile vehicle as described in step S16 in Figure 1. According to at least two mobile vehicles estimated from the above at least two reference points, the position of the mobile vehicle is calculated as follows: ,
綜上所述,本發明所提供之應用路側特徵辨識之圖資定位系統及方法係利用低成本的空拍圖,擷取高精度的道路影像地圖,與行進中擷取的周圍道路環境所製作的點雲圖進行資訊空間的疊合,以分類出道路空間和路側空間、動態物件和靜態物件,並將動態物件及空無一物的路側空間刪除,大幅減少資料量,且只需要二路側特徵點做為參考點就可以計算出移動載具的航向角度及位置,運算複雜度低但可靠度高,不使用衛星定位系統但精準度可達到公分為計,適用於自動駕駛車輛之導航定位。In summary, the map data positioning system and method using roadside feature recognition provided by the present invention are made by using low-cost aerial images to capture high-precision road image maps and the surrounding road environment captured during travel. The point cloud image is used to superimpose the information space to classify the road space and the roadside space, dynamic objects and static objects, and delete the dynamic objects and the empty roadside space, which greatly reduces the amount of data, and only requires two roadside features Point as a reference point can calculate the heading angle and position of the mobile vehicle. The calculation complexity is low but the reliability is high. The satellite positioning system is not used but the accuracy can reach centimeters. It is suitable for the navigation and positioning of autonomous vehicles.
唯以上所述者,僅為本發明之較佳實施例而已,並非用來限定本發明實施之範圍。故即凡依本發明申請範圍所述之特徵及精神所為之均等變化或修飾,均應包括於本發明之申請專利範圍內。Only the above are only preferred embodiments of the present invention, and are not used to limit the scope of implementation of the present invention. Therefore, all equivalent changes or modifications made in accordance with the characteristics and spirit of the application scope of the present invention shall be included in the patent application scope of the present invention.
10:道路空間 12:第一路側空間 14:第二路側空間 20:環境感測裝置 22:圖資定位系統 222:資料庫 224:路側特徵辨識模組 226:移動載具航向角度估測模組 228:移動載具位置估算模組10: Road space 12: First roadside space 14: Second roadside space 20: Environmental sensing device 22: Map data positioning system 222: database 224: Roadside feature recognition module 226: Mobile vehicle heading angle estimation module 228: Mobile Vehicle Position Estimation Module
第1圖為本發明應用路側特徵辨識之圖資定位方法之流程圖; 第2圖為本發明中建立定位圖資之細部流程圖; 第3A圖至第3D圖為定位圖資建立之流程示意圖; 第4圖為本發明中圖資定位系統之方塊圖; 第5圖為本發明中應用定位圖資辨識路側特徵點之流程圖; 第6圖為本發明中計算移動載具航向角度及位置之示意圖。Figure 1 is a flow chart of the map data positioning method using roadside feature recognition according to the present invention; Figure 2 is a detailed flow chart of the establishment of location map data in the present invention; Figures 3A to 3D are schematic diagrams of the process of establishing location map data; Figure 4 is a block diagram of the China Map Information Positioning System of the present invention; Figure 5 is a flowchart of identifying roadside feature points using location map data in the present invention; Figure 6 is a schematic diagram of calculating the heading angle and position of the mobile vehicle in the present invention.
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