本文選題：視覺定位 + 視覺里程計(jì)�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：移動(dòng)機(jī)器人的自定位問題是機(jī)器人領(lǐng)域的一個(gè)關(guān)鍵問題。攝像頭作為移動(dòng)機(jī)器人的“眼睛”,由于其體積小、成本低、應(yīng)用場景廣的特點(diǎn)而得到了廣泛的應(yīng)用。由于傳統(tǒng)的定位手段比如GPS、慣導(dǎo)在城市、室內(nèi)環(huán)境下定位結(jié)果不穩(wěn)定,近些年來,視覺定位越來越受到了廣泛的關(guān)注。視覺里程計(jì)是視覺定位中一種經(jīng)典的方法,但是,視覺里程計(jì)存在累積誤差的問題,無法在實(shí)際的長距離中進(jìn)行運(yùn)用。在本文中,我們提出了一種新穎的基于多位置聯(lián)合濾波和路網(wǎng)地圖的定位算法來解決視覺里程計(jì)的誤差累積問題。與以往的基于地圖的定位方法不同,我們認(rèn)為基于“點(diǎn)-線”的地圖表示方法并不能很精確地表示地圖,我們的方法充分考慮到地圖的這種不精確性,不會(huì)強(qiáng)制將濾波后的車輛軌跡糾正到地圖上。將視覺里程計(jì)定位結(jié)果和路網(wǎng)地圖信息作為初始輸入,在多位置聯(lián)合粒子濾波框架下設(shè)計(jì)了一種靈活的定位優(yōu)化算法。該算法將視覺里程計(jì)的定位結(jié)果作為初步的軌跡與路網(wǎng)地圖進(jìn)行匹配濾波,使定位結(jié)果能在視覺里程計(jì)定位和路網(wǎng)地圖匹配定位中進(jìn)行合適的平衡。由于只在車輛軌跡的拐點(diǎn)處進(jìn)行濾波,因此相對(duì)于視覺里程計(jì),算法只增加了很少的計(jì)算量。在KITTI數(shù)據(jù)集和校園環(huán)境中采集的數(shù)據(jù)進(jìn)行了多個(gè)實(shí)驗(yàn),并與其他定位算法進(jìn)行了定量的比較,實(shí)驗(yàn)結(jié)果都表明了所提算法的準(zhǔn)確性和魯棒性。
[Abstract]:The self-localization of mobile robot is a key problem in the field of robot. As the "eye" of mobile robot, camera is widely used because of its small size, low cost and wide application scene. Because of the instability of traditional positioning methods such as GPS, inertial navigation in cities and indoor environment, visual positioning has been paid more and more attention in recent years. Visual odometer is a classical method in visual positioning. However, there is a problem of accumulated error in visual odometer, which can not be used in actual long distance. In this paper, we propose a novel localization algorithm based on multi-position joint filtering and road map to solve the error accumulation problem of vision odometer. Different from the previous map based localization methods, we think that the map representation method based on "point-line" can not represent the map accurately, and our method fully takes into account the imprecision of the map. The filtered vehicle trajectory will not be forced to be corrected onto the map. The location results of vision odometer and road map information are taken as the initial input, and a flexible location optimization algorithm is designed under the framework of multi-position joint particle filter. The algorithm uses the location result of visual odometer as the initial path and the road map to match and filter, so that the location results can be properly balanced between vision odometer location and road map matching location. Due to filtering only at the inflection point of the vehicle trajectory, the algorithm increases only a small amount of computation compared to the visual mileometer. The data collected in KITTI data set and campus environment are tested and compared quantitatively with other localization algorithms. The experimental results show that the proposed algorithm is accurate and robust.
【學(xué)位授予單位】：浙江大學(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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本文編號(hào)：1887020