本文選題：物體識(shí)別　切入點(diǎn)：SLAM　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著機(jī)器人技術(shù)的進(jìn)步,移動(dòng)服務(wù)機(jī)器人的需求也越來(lái)越大。移動(dòng)機(jī)器人的環(huán)境感知技術(shù)也越來(lái)越成為研究熱點(diǎn)。作為環(huán)境感知的關(guān)鍵技術(shù)SLAM技術(shù),近些年也有了很大的進(jìn)步,機(jī)器人的實(shí)時(shí)定位與地圖構(gòu)建能力得到大幅提升。但是移動(dòng)機(jī)器人在未知環(huán)境中首次自主探索的效率低下,構(gòu)建地圖魯棒性仍然較差,地圖構(gòu)建精度較低,地圖的后期使用范圍有限。本文以構(gòu)建實(shí)用性較強(qiáng)的機(jī)器人環(huán)境感知系統(tǒng)為導(dǎo)向,研究了環(huán)境引導(dǎo)探索方法、地圖構(gòu)建和地圖智能語(yǔ)義標(biāo)注。為了解決機(jī)器人自主環(huán)境探索效率低的問題,提出了一種自然人機(jī)交互系統(tǒng)來(lái)引導(dǎo)機(jī)器人完成環(huán)境探索。自然人機(jī)交互可以有效縮減使用機(jī)器人的學(xué)習(xí)成本,讓用戶根據(jù)已有的經(jīng)驗(yàn)完成對(duì)機(jī)器人的操作使用。使用虛擬現(xiàn)實(shí)技術(shù)作為視覺反饋,使用手勢(shì)交互作為控制方法,來(lái)實(shí)現(xiàn)人與機(jī)器人的自然交互。在機(jī)器人進(jìn)行環(huán)境探索的同時(shí)需要完成機(jī)器人對(duì)環(huán)境的地圖構(gòu)建任務(wù)。為了能夠獲取更詳細(xì)的環(huán)境信息,本課題將RGBD SLAM和ORB SLAM進(jìn)行融合,實(shí)現(xiàn)了稠密三維點(diǎn)云地圖構(gòu)建算法。為了讓機(jī)器人能夠真正理解環(huán)境信息,本課題提出了一種物體分割定位和特征提取算法。算法使用晶格分析的方法來(lái)對(duì)空間中的云點(diǎn)進(jìn)行分析來(lái)獲取可能的物體分布進(jìn)而實(shí)現(xiàn)物體的分割。在分割完成后根據(jù)晶格信息完成物體的定位和特征描述。物體識(shí)別算法借鑒詞袋模型對(duì)物體進(jìn)行識(shí)別,通過(guò)物體識(shí)別算法即可獲得物體的類別信息。將物體的類別信息和定位信息綁定構(gòu)成語(yǔ)義索引,即可完成語(yǔ)義地圖的構(gòu)建。最后本文構(gòu)建了完整的實(shí)驗(yàn)系統(tǒng),驗(yàn)證了自然人機(jī)交互系統(tǒng)的可行性和易用性。使用稠密三維點(diǎn)云地圖構(gòu)建算法構(gòu)建了環(huán)境地圖,并在此基礎(chǔ)上構(gòu)建了物體樣本集和訓(xùn)練集,驗(yàn)證了物體分割定位算法和識(shí)別算法的有效性和準(zhǔn)確率。
[Abstract]:With the development of robot technology, the demand of mobile service robot is more and more great. The environment sensing technology of mobile robot has become a hot research topic. As a key technology of environment perception, SLAM technology has made great progress in recent years. The real-time localization and map construction ability of the robot has been greatly improved, but the efficiency of the first autonomous exploration of the mobile robot in the unknown environment is low, the robustness of the map construction is still poor, and the map construction accuracy is low. The use of map in the later stage is limited. In this paper, the exploration method of environment guidance is studied, which is guided by the construction of a practical robot environment perception system. Map construction and map intelligent semantic annotation. In order to solve the problem of low efficiency of robot autonomous environment exploration, A natural human-computer interaction system is proposed to guide the robot to complete the environmental exploration. The natural human-computer interaction can effectively reduce the learning cost of using the robot. Using virtual reality technology as visual feedback and gesture interaction as control method. In order to realize the natural interaction between human and robot, we need to complete the mapping task of robot to environment while exploring the environment. In order to obtain more detailed environment information, we combine RGBD SLAM and ORB SLAM. The algorithm of constructing dense 3D point cloud map is implemented. In order to make the robot really understand the environment information, In this paper, an algorithm of object segmentation and feature extraction is proposed. The algorithm uses lattice analysis to analyze cloud points in space to obtain possible object distribution and to achieve object segmentation. Then the object location and feature description are completed according to the lattice information. The object recognition algorithm uses the word bag model to identify the object. The object classification information can be obtained by the object recognition algorithm. The semantic index can be constructed by binding the object category information and the location information to complete the construction of the semantic map. Finally, a complete experimental system is constructed in this paper. The feasibility and ease of use of the natural human-computer interaction system are verified. The environment map is constructed by using dense 3D point cloud map construction algorithm, and the object sample set and training set are constructed on the basis of this algorithm. The validity and accuracy of object segmentation algorithm and recognition algorithm are verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP242

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