本文選題：語義地圖 + 語義-CVFH樣本庫�。� 參考：《山東大學》2017年碩士論文

【摘要】：環(huán)境地圖的語義描述程度是影響機器人智能化的關(guān)鍵要素,復(fù)雜環(huán)境中的"自主"語義感知能力是機器人導(dǎo)航智能化的重要體現(xiàn)。課題設(shè)計基于云的環(huán)境語義獲取框架,確定按照類別劃分的樣本庫方案、利用云端資源與樣本庫擴展本體知識庫并實現(xiàn)語義地圖的構(gòu)建,將地圖對物品、區(qū)域等的認知應(yīng)用到機器人服務(wù)任務(wù)中來。針對機器人在服務(wù)任務(wù)中難以"自主"獲得復(fù)雜環(huán)境中物品語義問題,設(shè)計基于云的環(huán)境語義獲取框架。"云"雖然可以為我們提供海量資源,但這些資源并不能直接用于獲取物品語義,需要進一步形成適用于獲取語義信息的數(shù)據(jù)模型�？蚣芑诰矸e神經(jīng)網(wǎng)絡(luò)、支持向量機和點云技術(shù)形成語義-CVFH(Cluster Viewpoint-Feature Histogram)樣本庫,用于多樣化場景的物品語義查詢。為了提高導(dǎo)航精度并使地圖更加人性化,將獲取語義后的物品分為標識物品與歸屬物品(包括顯性歸屬物品與隱性歸屬物品),其中標識物品與顯性歸屬物品通過查詢語義-CVFH樣本庫獲得物品語義并分別存入標識庫、歸屬庫。標識歸屬物品位置關(guān)系通過對PCL(Point Cloud Library)點云進行處理確定。-復(fù)雜環(huán)境包含多種多樣的物品,物品與環(huán)境的知識化是機器人實現(xiàn)智能化的重要因素。建立人、物品與房間為基礎(chǔ)的本體模型,定義實例屬性、對象屬性并創(chuàng)建實例,利用云端資源、規(guī)則完善模型并持久化存儲形成本體知識庫。標識物品、歸屬物品與房間功能確定后用于本體知識庫的發(fā)育,使機器人具備一定的推理能力,擁有類似于人的"常識"。在語義-CVFH樣本庫與本體知識庫的基礎(chǔ)上,機器人根據(jù)結(jié)構(gòu)化環(huán)境信息(包括二維柵格地圖與自定位),在物理坐標處分割場景點云得到物品點云并上傳至私有云,通過查詢語義-CVFH樣本庫獲得物品語義;在結(jié)構(gòu)化地圖當前位置關(guān)聯(lián)標識物品并通過XML標記語言記錄此位置的標識歸屬位置關(guān)系形成語義地圖,機器人根據(jù)需求由私有云下載語義地圖用于執(zhí)行服務(wù)任務(wù)。本文所提出的方法將人與機器人工作環(huán)境緊密關(guān)聯(lián),云端的豐富環(huán)境數(shù)據(jù)與機器人地圖緊密關(guān)聯(lián),使機器人地圖擬人化與知識化,為機器人提供智能化任務(wù)奠定了基礎(chǔ)。該工作對深化和完善云機器人領(lǐng)域研究、加速其發(fā)展應(yīng)用具有重要科學意義和實用價值。
[Abstract]:The semantic description of the environment map is the key factor affecting the robot intelligence. The "autonomous" semantic perception ability in complex environment is an important embodiment of the intelligent robot navigation. The subject design is based on the framework of the cloud environment semantics, determines the sample database scheme divided according to the category, and uses the cloud resources and the sample library to extend the ontology. The knowledge base and the construction of semantic map are applied to the task of robot service. It is difficult to obtain the semantic problems of the objects in the complex environment by the robot in the service task. Resources can not be used directly to obtain item semantics. It needs to further form a data model suitable for obtaining semantic information. The framework is based on convolution neural network, support vector machine and point cloud technology to form a semantic -CVFH (Cluster Viewpoint-Feature Histogram) sample library for multi sample semantic query of objects. In order to improve navigation essence, The objects and belongings (including the dominant belongings and the hidden belongings) are divided into the identification items and the belongings (including the dominant belongings and the hidden belongings), in which the items and the dominant belongings are obtained by the query semantic -CVFH sample library to obtain the object semantics and be stored separately in the library. The PCL (Point Cloud Library) point cloud is processed and determined. - complex environment contains a variety of objects. The knowledge of goods and environment is an important factor in the intelligentization of robots. Establish the ontology model based on people, objects and rooms, define instance attributes, object and create instances, use cloud resources, and improve the model with rules. And persisted to form the ontology knowledge base. Identifying objects, belongings and room functions are used for the development of ontology knowledge base, so that the robot has certain reasoning ability and has similar "common sense". Based on the semantic -CVFH sample library and ontology knowledge base, the robot is based on the structured environment information (including two-dimensional grid). Map and self positioning), divide the scene point cloud at the physical coordinates to get the object point cloud and upload it to the private cloud, and obtain the object semantics by querying the semantic -CVFH sample library. The semantic map is formed for the identification of the objects in the current position of the structured map and the identification of the location by the XML markup language. In order to download the semantic map from the private cloud, the semantic map is used to carry out the service task. The method proposed in this paper is closely related to the working environment of the robot. The rich environmental data in the cloud is closely related to the robot map, which makes the robot map personified and knowledgeable, and lays the foundation for the robot to provide intelligent tasks. The work is deepened and perfected. It is of great scientific significance and practical value to accelerate the development and application of cloud robots.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

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