【摘要】：為了解決機(jī)器人未知環(huán)境導(dǎo)航過程中的多源、異構(gòu)傳感器空間一致性觀測(cè)問題,提出了基于動(dòng)態(tài)和靜態(tài)環(huán)境對(duì)象觀測(cè)一致性約束的攝像機(jī)與激光測(cè)距傳感器聯(lián)合標(biāo)定優(yōu)化方法。利用協(xié)方差交集方法實(shí)現(xiàn)運(yùn)動(dòng)目標(biāo)圖像平面方向狀態(tài)融合,同時(shí)采用卡爾曼濾波和概率數(shù)據(jù)關(guān)聯(lián)濾波實(shí)現(xiàn)一對(duì)一和一對(duì)多信息源的靜態(tài)角點(diǎn)特征圖像平面方向狀態(tài)融合;在此基礎(chǔ)上,利用動(dòng)態(tài)和靜態(tài)物體融合前與融合后狀態(tài)誤差構(gòu)造優(yōu)化目標(biāo)函數(shù),并利用非線性優(yōu)化方法實(shí)現(xiàn)標(biāo)定參數(shù)優(yōu)化。實(shí)驗(yàn)結(jié)果表明,該設(shè)計(jì)方法能夠提高多傳感器環(huán)境觀測(cè)的一致性水平,驗(yàn)證了該方法的有效性。
[Abstract]:In order to solve the problem of spatial consistency observation of multi-source and heterogeneous sensors during robot navigation in unknown environment, a joint calibration optimization method of camera and laser rangefinder based on observation consistency constraints of dynamic and static environment objects is proposed. The covariance intersection method is used to realize the plane direction state fusion of moving target image. At the same time, Kalman filter and probabilistic data correlation filter are used to realize the plane direction state fusion of one-to-one and one-to-many information sources. On this basis, the optimization objective function is constructed by using the state errors before and after the fusion of dynamic and static objects, and the calibration parameters are optimized by nonlinear optimization method. The experimental results show that the design method can improve the consistency of multi-sensor environmental observation and verify the effectiveness of the method.
【作者單位】： 火箭軍工程大學(xué);
【基金】：國家自然科學(xué)基金項(xiàng)目(61503389) 陜西省自然科學(xué)基金項(xiàng)目(2016JM6061)
【分類號(hào)】：TP212;TP242

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本文編號(hào)：2254554