本文爲美國俄亥俄州立大學（作者：J.N. (Nikki) Markiel）的博士論文，共216頁。
生物駕馭環境的能力是生命的一大奧祕。人類，甚至從很小的時候，就可以獲取周圍環境的數據，確定物體是可移動的還是固定的，並在無窮小的時間跨度內識別出開闊的空間，分離出靜止和非靜止的物體，以最小的努力向另一個位置運動。在很長的一段時間內，人類可以僅僅基於地標的相對位置來回憶物體的位置，並重復導航任務。儘管在過去的半個世紀裏進行了大量的研究工作，我們在自動駕駛汽車上模擬這一複雜過程的能力仍然不完整。
自主車輛依靠各種電子傳感器來獲取有關其環境的數據；挑戰是將這些數據轉換爲支持導航目標的信息。歷史上，許多傳感器數據僅限於二維（2D）情況；最近的技術發展，如激光測距和3D聲納正在將數據採集擴展到全三維（3D）採集。本文的目標是開發一種算法，以支持在未知環境下，在動態運動目標存在的情況下，將三維測距數據轉換爲導航求解。該算法反映了利用三維測距技術實現自主導航的最初嘗試之一，並提供了一個能夠完成以下目標的系統：•環境中靜態和非靜態元素的分離•基於靜態元素距離測量的導航
本研究擴展了三個主題的知識體系。
1） 第一種是開發一種通用方法，以從後續數據集中的m個特徵中識別初始數據集中的n個特徵，前提是這兩個數據集都是通過3D測距傳感器獲取的。在試圖鏈接重疊的數據集時實現這一目標，特別是在二維數據集方面，一直是一個難題。
2） 其次，提出了一種新的分割離散三維距離測量的方法。
3） 最後，本研究提出了一種方法，以支持先前因缺乏位置更新而無法在自主車輛上使用的環境中導航。這個問題在導航領域是衆所周知的；雖然全球定位系統（GPS）提供了極好的位置信息，但它們的信號在各種條件下都可能變得不可用，例如室內或地下位置、密集的城市環境或干擾的信號環境。
目前對機器人操作的研究很少涉及未知環境下的操作概念，而且幾乎從不嘗試在非靜態物體存在的情況下導航。將導航解決方案擴展到這些限制之外的能力進一步拓展了自主導航的可能性，並推進了導航領域的發展。目前的算法不能爲不確定的時間段提供導航解算，但可以在不借助GPS定位的情況下，擴大導航的可行範圍。雖然這項研究不可能宣稱能解決自主導航的問題，但它代表了朝着開發機器模擬認知導航的願景邁出的重要一步。由於我們只能站在巨人的肩膀上看得更遠，希望這項研究有一天能使另一位研究人員看到真正的自主導航的成就。
The ability of living creatures to navigatetheir environment is one of the great mysteries of life. Humans, even from anearly age, can acquire data about their surroundings, determine whether objectsare movable or fixed, and identify open space, separate static and non-staticobjects, and move towards another location with minimal effort, ininfinitesimal time spans. Over extended time periods humans can recall thelocation of objects and duplicate navigation tasks based purely on relativepositioning of landmarks. Our ability to emulate this complex process inautonomous vehicles remains incomplete, despite significant research effortsover the past half century. Autonomous vehicles rely on a variety of electronicsensors to acquire data about their environment; the challenge is to transformthat data into information supporting the objective of navigation.Historically, much of the sensor data was limited to the two dimensional (2D)instance; recent technological developments such as Laser Ranging and 3D Sonarare extending data collection to full three dimensional (3D) acquisition. Theobjective of this dissertation is the development of an algorithm to supportthe transformation of 3D ranging data into a navigation solution within unknownenvironments, and in the presence of dynamically moving objects. The algorithmreflects one of the very first attempts to leverage the 3D ranging technologyfor the purpose of autonomous navigation, and provides a system which enablesthe ability to complete the following objectives: ·Separation of static and non-static elements in the environment ·Navigation based upon the range measurements of static elements This researchextends the body of knowledge in three primary topics. 1) The first is thedevelopment of a general method to identify n features in an initial data setfrom m features in a subsequent data set, given that both data sets areacquired via 3D ranging sensors. Accomplishing this objective, particularlywith respect to 2D datasets, has long been a difficult proposition whenattempting to link overlapping data sets. 2) Secondly, an innovativemethodology to segment a set of discrete 3D range measurements is presented. 3)Finally, the research develops a methodology to support navigation inenvironments previously infeasible for autonomous vehicles due to lack ofposition updates. This problem is well known in the navigation field; whileGlobal Positioning Systems (GPS) provide excellent positional information,their signals can become unavailable in a wide variety of conditions, such asindoor or underground localities, dense urban settings, or jammed signalenvironments. Current research in robotic manipulation rarely addresses theconcept of operations within an unknown environment, and virtually neverattempts navigation in the presence of non-static objects. The ability toextend the navigation solution beyond these limitations extends the possibilitiesfor autonomous navigation and advances the field of navigation. The currentalgorithm cannot provide a navigation solution for an indefinite time period;it can extend the feasible extent of navigation without benefit of GPSpositioning. While this research could not possibly claim to solve the problemof autonomous navigation, it represents an important step towards the vision ofdeveloping a machine to emulate cognitive navigation. As we see farther only bystanding on the shoulders of giants, it is hoped that this research willsomeday enable another researcher to see the achievement of true autonomousnavigation.
- 引言
- 三維數據與算法
- 算法實現
- 支撐技術回顧
- 實驗數據採集
- 本文算法在實驗數據和結果中的應用
- 結論與觀察
附錄 絕對定向：霍恩閉式解
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