本文選題：二維激光雷達(dá) + 聚類算法�。� 參考：《北京理工大學(xué)》2016年碩士論文

【摘要】：隨著現(xiàn)代化進(jìn)程的加速推進(jìn),無(wú)人運(yùn)動(dòng)平臺(tái)扮演著日益重要的角色,其中環(huán)境感知技術(shù)是無(wú)人運(yùn)動(dòng)平臺(tái)中關(guān)鍵技術(shù)之一。為此,本文以旅行家二號(hào)試驗(yàn)車、二維激光雷達(dá)以及慣性傳感集成模塊為實(shí)驗(yàn)平臺(tái),針對(duì)無(wú)人運(yùn)動(dòng)平臺(tái)的障礙物的檢測(cè)以及無(wú)人運(yùn)動(dòng)平臺(tái)的定位問題展開了深入討論。在障礙物檢測(cè)方面,本文以二維激光雷達(dá)數(shù)據(jù)為基礎(chǔ),首先利用閾值濾波和改進(jìn)的中值濾波算法對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,然后借助激光雷達(dá)數(shù)據(jù)的上下文豐富障礙物的數(shù)據(jù)量,最后通過(guò)聚類算法對(duì)障礙物進(jìn)行了識(shí)別。同時(shí)本文提出利用凸包算法解決了聚類過(guò)程中存在的冗余聚類信息,這種方法大大提高了系統(tǒng)的魯棒性。針對(duì)無(wú)人運(yùn)動(dòng)平臺(tái)的定位問題。本文首先對(duì)無(wú)人運(yùn)動(dòng)平臺(tái)的運(yùn)動(dòng)模型進(jìn)行了分析,并確定了影響無(wú)人運(yùn)動(dòng)平臺(tái)定位準(zhǔn)確度的因素,然后通過(guò)IF-THEN模糊預(yù)測(cè)模型對(duì)無(wú)人運(yùn)動(dòng)平臺(tái)的定位誤差進(jìn)行了估計(jì)。最后通過(guò)二維激光雷達(dá)前后幀特征數(shù)據(jù)對(duì)無(wú)人運(yùn)動(dòng)平臺(tái)的定位估計(jì)進(jìn)行了最優(yōu)處理。數(shù)據(jù)結(jié)果顯示基于模糊預(yù)測(cè)和二維激光雷達(dá)特征數(shù)據(jù)的定位算法要優(yōu)于單純依靠慣性器件的定位信息。在無(wú)人運(yùn)動(dòng)平臺(tái)的硬件及軟件系統(tǒng)方面,本文以旅行家二號(hào)試驗(yàn)車為移動(dòng)平臺(tái),通過(guò)SICK二維激光獲取環(huán)境感知信息、ASV940慣性傳感器集成模塊獲取運(yùn)動(dòng)信息、移動(dòng)電腦進(jìn)行算法運(yùn)行。在軟件方面,本文基于VS2010平臺(tái),實(shí)現(xiàn)了激光雷達(dá)數(shù)據(jù)實(shí)時(shí)采集功能界面、旅行家二號(hào)試驗(yàn)車的運(yùn)動(dòng)控制界面、慣性傳感器集成模塊信息顯示界面以及算法功能選擇界面。在實(shí)驗(yàn)過(guò)程中,本文分別在動(dòng)態(tài)、靜態(tài)環(huán)境中對(duì)障礙物識(shí)別算法進(jìn)行了驗(yàn)證。實(shí)驗(yàn)表明,兩類環(huán)境中CBFD聚類算法(基于峰值點(diǎn)的聚類算法)在聚類合理性上均優(yōu)于K均值聚類算法和ISODATA聚類算法(迭代自組織數(shù)據(jù)分析算法)。
[Abstract]:With the acceleration of the modernization process, unmanned sports platform plays an increasingly important role, among which environmental awareness technology is one of the key technologies in unmanned sports platform. Therefore, this paper takes Traveler No. 2 test vehicle, two-dimensional lidar and inertial sensor integration module as the experimental platform, and discusses the obstacle detection of unmanned motion platform and the positioning of unmanned motion platform in depth. In the aspect of obstacle detection, based on the two-dimensional lidar data, the threshold filter and the improved median filtering algorithm are first used to preprocess the data, and then the context of the lidar data is used to enrich the amount of obstacle data. Finally, the obstacles are identified by clustering algorithm. At the same time, a convex hull algorithm is proposed to solve the redundant clustering information in the clustering process, which greatly improves the robustness of the system. This paper aims at the localization of unmanned platform. In this paper, the motion model of unmanned motion platform is analyzed, and the factors that affect the accuracy of unmanned motion platform are determined, and then the positioning error of unmanned platform is estimated by IF-THEN fuzzy prediction model. Finally, the location estimation of the unmanned moving platform is optimized by using the feature data of the front and rear frames of the two dimensional lidar. The results show that the localization algorithm based on fuzzy prediction and two-dimensional lidar feature data is superior to the location information based on inertial devices alone. In the aspect of hardware and software system of unmanned motion platform, this paper takes Traveler No. 2 as the mobile platform, acquires the environment sensing information by SICK two-dimensional laser and ASV940 inertial sensor integration module to obtain motion information. The mobile computer runs the algorithm. In terms of software, based on VS2010 platform, this paper realizes the real-time data acquisition interface of lidar, the motion control interface of traveller No. 2 test vehicle, the information display interface of inertial sensor integration module and the algorithm function selection interface. In the process of experiment, the algorithm of obstacle recognition is verified in dynamic and static environment. Experimental results show that CBFD clustering algorithm (peak point based clustering algorithm) is superior to K-means clustering algorithm and ISODATA clustering algorithm (iterative self-organizing data analysis algorithm) in clustering rationality.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN958.98

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