【摘要】：越來(lái)越多的應(yīng)用都需要對(duì)工作在室內(nèi)和室外的人進(jìn)行跟蹤定位,在醫(yī)療,游戲、虛擬現(xiàn)實(shí)等領(lǐng)域,獲取人體運(yùn)動(dòng)過(guò)程中的數(shù)據(jù)并進(jìn)行跟蹤和重建,可以對(duì)人體運(yùn)動(dòng)進(jìn)行描述以獲得更多潛在的信息。在臨床康復(fù)治療中,跟蹤室外環(huán)境下病人的運(yùn)動(dòng)狀況,可以使得病人的活動(dòng)被實(shí)時(shí)監(jiān)控并得到及時(shí)改正;在游戲領(lǐng)域,創(chuàng)造出逼真的虛擬世界,使其中的人物具有逼真的運(yùn)動(dòng)效果是動(dòng)畫(huà)制作的成功關(guān)鍵;在人際交互領(lǐng)域,智能機(jī)器不僅需要通過(guò)語(yǔ)音與人直接交流,還需要有運(yùn)動(dòng),姿態(tài)等更加自然的交互方式;在更多現(xiàn)實(shí)領(lǐng)域內(nèi),人們期望獲得日常生活中運(yùn)動(dòng)的時(shí)間長(zhǎng)短,步行、奔跑的軌跡和運(yùn)動(dòng)的規(guī)律來(lái)進(jìn)一步對(duì)自身活動(dòng)規(guī)律進(jìn)行調(diào)整。因此,實(shí)時(shí)記錄,無(wú)線傳輸和長(zhǎng)時(shí)間跟蹤人體運(yùn)動(dòng)的研究分析受到了廣泛關(guān)注。大多數(shù)人體運(yùn)動(dòng)都包含了重復(fù)的可識(shí)別周期,運(yùn)動(dòng)中的加速度和速度測(cè)量值在一個(gè)周期內(nèi)的特定時(shí)期為零值,對(duì)周期性步態(tài)規(guī)律的識(shí)別能夠幫助我們將跟蹤信號(hào)中的誤差減少,得到更加精確的位置信息。為了使定位的精度不易受到外界的干擾并且不依賴于其他基礎(chǔ)建設(shè)的支持,慣性傳感系統(tǒng)有著完全自主定位的優(yōu)點(diǎn),測(cè)量單元既不會(huì)受到運(yùn)動(dòng)模型的影響也不會(huì)受到定位環(huán)境的影響,因而受到了廣泛的關(guān)注。然而,受到低成本慣性傳感器件本身誤差的限制,定位信息并不準(zhǔn)確,所以急需要一種計(jì)算復(fù)雜度低且定位精度高的方案。本文首先簡(jiǎn)要地介紹和分析了基于捷聯(lián)式慣性導(dǎo)航中姿態(tài)解算算法的研究現(xiàn)狀與特點(diǎn),將畢卡法與有加速度和磁力計(jì)融合補(bǔ)償誤差的姿態(tài)算法進(jìn)行性能對(duì)比,分析了不同姿態(tài)解算針對(duì)不同運(yùn)動(dòng)模式的優(yōu)缺點(diǎn)。在此基礎(chǔ)上,深入考察了基于梯度下降的姿態(tài)更新算法在時(shí)間復(fù)雜度和性能上的提高,并據(jù)此給出了適合人體運(yùn)動(dòng)模式下自適應(yīng)的姿態(tài)解算算法。然后提出了將慣性測(cè)量單元固定在鞋上的運(yùn)動(dòng)跟蹤方式,結(jié)合步行者運(yùn)動(dòng)中傳感器信號(hào)周期性變化的特點(diǎn)來(lái)提高運(yùn)動(dòng)軌跡跟蹤精度的算法,給出了基于頻域的步態(tài)周期計(jì)算方式以及步態(tài)周期中零速度更新算法。最后,在實(shí)際環(huán)境中,利用差分法提取有效數(shù)據(jù)片段,采用多采樣率處理來(lái)自適應(yīng)運(yùn)動(dòng)模式。研究結(jié)果表明,經(jīng)過(guò)預(yù)處理和多采樣率處理后,每個(gè)有效運(yùn)動(dòng)時(shí)間片段內(nèi)的數(shù)據(jù)使用于不同的姿態(tài)算法,能夠得到更加平滑和復(fù)雜度低的定位。結(jié)合運(yùn)動(dòng)模式的特點(diǎn),將傳感器安裝在鞋上對(duì)步行者運(yùn)動(dòng)軌跡跟蹤過(guò)程中,通過(guò)零速度檢測(cè)進(jìn)一步降低漂移誤差實(shí)現(xiàn)對(duì)實(shí)際環(huán)境中運(yùn)動(dòng)軌跡的有效跟蹤。
[Abstract]:More and more applications need to track and locate people who work indoors and outdoors. In the fields of medicine, games, virtual reality and so on, we need to obtain and track and reconstruct the data of human body movement. Human motion can be described for more potential information. In clinical rehabilitation, tracking patients' movements in outdoor environments allows them to be monitored in real time and corrected in time; in the field of games, a realistic virtual world is created. In the field of interpersonal interaction, intelligent machines not only need to communicate with people directly through voice, but also need more natural interaction methods, such as movement and posture, etc. In more practical fields, people expect to obtain the time of daily movement, the track of walking, running and the law of movement to further adjust the law of their own activities. Therefore, the research and analysis of real-time recording, wireless transmission and long-term tracking of human motion have received wide attention. Most human movements contain repeated identifiable cycles, the acceleration and velocity measurements in motion are zero at a particular period of the cycle, and the identification of periodic gait patterns can help us reduce the errors in tracking signals. Get more accurate location information. In order to make the positioning accuracy difficult to be disturbed by the outside world and not dependent on the support of other infrastructure, the inertial sensing system has the advantage of completely autonomous positioning. The measurement unit is not affected by the motion model or the location environment, so it has been paid more and more attention. However, due to the error of the low cost inertial sensor, the location information is not accurate, so a scheme with low computational complexity and high positioning accuracy is urgently needed. Firstly, this paper briefly introduces and analyzes the research status and characteristics of attitude calculation algorithm based on strapdown inertial navigation, and compares the performance of Bika algorithm with attitude algorithm with acceleration and magnetometer fusion compensation error. The advantages and disadvantages of different attitude solutions for different motion modes are analyzed. On this basis, the improvement of the time complexity and performance of the attitude updating algorithm based on gradient descent is investigated in depth, and an adaptive attitude algorithm suitable for human motion mode is presented. Then, an algorithm is proposed to improve the tracking accuracy of the motion track by fixing the inertial measurement unit on the shoe and combining the characteristics of periodic changes of the sensor signal in the walker motion. The method of calculating gait period in frequency domain and the algorithm of zero velocity updating in gait period are presented. Finally, in the actual environment, the effective data segment is extracted by differential method, and the multi-sampling rate is used to deal with the adaptive motion pattern. The results show that after preprocessing and multi-sampling rate processing, the data in each effective moving time segment can be used in different attitude algorithms, which can achieve smoother and less complexity localization. According to the characteristics of motion mode, sensors are installed on shoes to track the walker's motion track, and the drift error is further reduced by zero velocity detection to realize the effective tracking of the moving track in the actual environment.
【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】：2015
【分類(lèi)號(hào)】：TN96

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本文編號(hào)：2154333