異常點云干擾下的車身構(gòu)件魯棒性配準方法

丁濤 吳浩 朱大虎

摘要：

點云配準是大型車身構(gòu)件位姿參數(shù)測量的關(guān)鍵方法，但現(xiàn)有算法在大量異常點云干擾下難以配準至有效位姿，從而導致匹配失真，進而無法保證后續(xù)機器人作業(yè)質(zhì)量。針對此問題，提出一種能夠有效抑制異常點云干擾的車身構(gòu)件魯棒性配準算法——魯棒函數(shù)加權(quán)方差最小化（RFWVM）算法。建立魯棒函數(shù)加權(quán)目標函數(shù)，通過施加隨迭代次數(shù)可變的動態(tài)權(quán)重來抑制配準過程中異常點云的影響，并由高斯牛頓法迭代完成剛性轉(zhuǎn)換矩陣的求解。以高鐵白車身側(cè)墻、汽車車門框為研究對象的試驗結(jié)果表明，較經(jīng)典的最近點迭代（ICP） 算法、方差最小化（VMM） 算法、加權(quán)正負余量方差最小化（WPMAVM）算法和去偽加權(quán)方差最小化（DPWVM）算法，所提出的RFWVM算法配準精度更高，能夠有效抑制各種異常點云對配準結(jié)果的影響，并具有更好的穩(wěn)定性和魯棒性，能夠有效實現(xiàn)各類車身構(gòu)件點云的精確配準。

關(guān)鍵詞：點云配準；異常點云干擾；魯棒函數(shù)；車身構(gòu)件；機器人視覺測量

中圖分類號：TP24

DOI：10.3969/j.issn.1004132X.2024.06.013

開放科學（資源服務）標識碼（OSID）：

Robust Registration Method for Vehicle Body Components under Abnormal

Point Cloud Interference

DING Tao1，2? WU Hao1，2? ZHU Dahu1，2

1.Hubei Longzhong Laboratory，Wuhan University of Technology Xiangyang Demonstration Zone，

Xiangyang，Hubei，441000

2.School of Automotive Engineering，Wuhan University of Technology，Wuhan，430070

Abstract： Point cloud registration was a key method for pose parameter measurement of large vehicle body components， but the existing algorithms were difficult to register to effective pose under a large number of abnormal point cloud interference， thereby resulting in matching distortion and inability to ensure the quality of subsequent robotic operations. To address the issue， a robust registration algorithm for vehicle body components， robust function weighted variance minimization（RFWVM） algorithm was proposed that might effectively suppress the interference of abnormal point cloud. A robust function weighted objective function was established， and the influences of abnormal point cloud in the registration processes were suppressed by applying dynamic weights that varied with the number of iterations. The rigid transformation matrix was solved iteratively by the Gauss-Newton method. The experimental results on the side walls of high-speed rail body and car door frames demonstrate that the proposed RFWVM algorithm has higher registration accuracy compared to classic algorithms， such as interactive closure point（ICP）， variance minimization（VMM）， weighted plus and minimum allowance variance minimization（WPMAVM）， de-pseudo-weighted variance minimization（DPWVM）， may effectively suppress the influences of various abnormal point clouds on registration results， and also behaves better stability and robustness. The method may effectively achieve the accurate registration of various vehicle body components.

Key words： point cloud registration; abnormal point cloud interference; robust function; vehicle body component; robotic vision measurement

收稿日期：20230615

基金項目：國家重點研發(fā)計劃（2022YFB4700501）;國家自然科學基金（51975443）;湖北隆中實驗室自主創(chuàng)新項目（2022ZZ-27）

0? 引言

以機器人為制造裝備執(zhí)行體的機器人加工技術(shù)是實現(xiàn)軌道交通、先進汽車等領(lǐng)域大型復雜構(gòu)件高效高品質(zhì)制造的主流加工模式［1-3］。在機器人執(zhí)行加工任務之前，通常需要利用視覺傳感器實現(xiàn)工件的位姿參數(shù)測量。目前，三維點云配準是計算大型復雜構(gòu)件位姿參數(shù)的通用方法［4］，該方法通過計算模型點云到測量點云之間的剛體轉(zhuǎn)換矩陣得到兩片點云之間的精確位姿轉(zhuǎn)換關(guān)系，以實現(xiàn)工件的位姿測量。……