Improved Qtree_ORB Algorithm

doi:10.3778/j.issn.1002-8331.2009-0263

Abstract

Abstract:

In visual SLAM, feature point extraction and accurate feature matching play an important role in robot pose inference. Aiming at the uneven distribution of feature points in the traditional ORB algorithm, the problem of clustering and the over-uniform feature points of the Qtree_ORB algorithm, an improved ORB feature extraction algorithm based on quadtree is proposed. Each layer of the image pyramid is adaptively meshed, and adaptive thresholds are used for feature point extraction. It limits the division depth of the quadtree according to the number of feature points extracted from each layer of the image pyramid to reduce redundant feature points. It sets a minimum threshold to reduce the extraction of low-quality feature points. The uniformity and matching performance of the improved algorithm are tested on the Mikolajczyk dataset, and the accuracy of the improved algorithm in the ORB-SALM2 system is tested on the TUM dataset. The results show that its uniformity and matching accuracy can be effectively improved by the improved algorithm. In the ORB-SLAM2 test, the trajectory accuracy and drift degree of the SLAM system can be effectively improved by the improved algorithm.

Key words: visual Simultaneous Localization And Mapping（SLAM）, Oriented Fast and Rotated Brief（ORB）, Qtree_ORB algorithm, uniformity

摘要：

在视觉SLAM中，特征点的提取和准确的特征匹配对机器人的位姿推断具有重要作用。针对传统ORB算法特征点分布不均匀，容易出现簇集的问题和Qtree_ORB算法特征点过均匀等问题，提出了一种基于四叉树改进的ORB特征提取算法。对每层图像金字塔进行自适应网格划分，采用自适应阈值来进行特征点提取；根据每层图像金字塔所提取特征点数目对四叉树的划分深度进行限制，减少冗余特征点；设定最小阈值来减少低质量特征点的提取；在Mikolajczyk数据集上对改进算法的均匀度和匹配性能进行测试，在TUM数据集上对改进算法在ORB-SALM2系统中的精度进行测试。结果表明改进算法能够有效提高其均匀度，并且保持着较高的匹配精度；在ORB-SLAM2测试中，改进算法能有效改进SLAM系统的轨迹精度和漂移程度。

关键词: 视觉同时定位与建图算法（SLAM）, ORB算法, Qtree_ORB算法, 均匀度

YANG Shiqiang, BAI Lele, ZHAO Cheng, WANG Guodong, LI Dexin. Improved Qtree_ORB Algorithm[J]. Computer Engineering and Applications, 2021, 57(20): 82-89.

杨世强，白乐乐，赵成，王国栋，李德信. 一种改进的Qtree_ORB算法[J]. 计算机工程与应用, 2021, 57(20): 82-89.

References

[1] BRESSON G，ALSAYED Z，LI Y，et al.Simultaneous locali-
zation and mapping：a survey of current trends in autonomous driving[J].IEEE Transactions on Intelligent Vehicles，2017，2（3）：194-220.
[2] LI R H，WANG S，GU D B.Ongoing evolution of visual SLAM from geometry to deep learning：challenges and opportunities[J].Cognitive Computation，2018，10（6）：875-889.
[3] 权美香，朴松昊，李国.视觉SLAM综述[J].智能系统学报，2016，11（6）：768-776.
QUAN M X，PIAO S H，LI G.An overview of visual SLAM[J].CAAI Transactions on Intelligent Systems，2016，11（6）：768-776.
[4] RUBLEE E，RABAUD V，KONOLIGE K，et al.ORB：an efficient alternative to SIFT or SURF[C]//Proceedings of IEEE International Conference on Computer Vision，Washington，USA，2011：2564-2571.
[5] SUN D Y，ZHANG S J，WANG Y X.Improved feature point extraction and mismatch eliminating algorithm[J].Systems Science & Control Engineering，2020，8（1）：11-21.
[6] MUR-ARTAL R，MONTIEL J M M，TARDOS J D.ORB-SLAM：a versatile and accurate monocular SLAM system[J].IEEE Transactions on Robotics，2015，31（5）：1147-1163.
[7] MUR-ARTAL R，MONTIEL J M M，TARDOS J D.ORB-SLAM2：an open-source SLAM system for monocular，stereo，and RGB-D cameras[J].IEEE Transactions on Robotics，2017，33（5）：1255-1262.
[8] YU C，LIU Z，LIU X J，et al.DS-SLAM：a semantic visual SLAM towards dynamic environments[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems（IROS），2018：1168-1174.
[9] BESCOS B，FACIL J M，CIVERA J，et al.DynaSLAM：tracking，mapping，and inpainting in dynamic scenes[J].IEEE Robotics and Automation Letters，2018，3（4）：4076-4083.
[10] 伍锡如，黄榆媛，王耀南.改进ORB特征的机器人RGB-D SLAM算法[J].计算机工程与应用，2020，56（14）：35-44.
WU X R，HUANG Y Y，WANG Y N.Robot RGB-D SLAM algorithm based on improved ORB feature[J].Computer Engineering and Applications，2020，56（14）：35-44.
[11] ROSTEN E，PORTER R，DRUMMOND T.Faster and better：a machine learning approach to corner detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2010，32（1）：105-119.
[12] CALONDER M，LEPETIT V，FUA P.BRIEF：binary robust independent elementary features[C]//Proceedings of European Conference on Computer Vision，Greece，2010，63（14）：778-792.
[13] 郭恩会，张小国，陈刚.自适应邻域测试的图像误匹配点剔除算法[J].计算机工程与应用，2019，55（14）：198-202.
GUO E H，ZHANG X G，CHEN G.Research on algorithm of eliminating mismatching points based on adaptive neighborhood test[J].Computer Engineering and Applications，2019，55（14）：198-202.
[14] LI L，YANG F，ZHU H，et al.An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells[J].Remote Sensing，2017，9（5）：1-16.
[15] 成怡，佟晓宇.基于改进ORB算法的移动机器人视觉SLAM方法研究[J].电子技术应用，2019，45（1）：10-18.
CHENG Y，TONG X Y.Research on visual SLAM method of mobile robot based on improved ORB algorithm[J].Electronic Technology and Application，2019，45（1）：10-18.
[16] STURM J，ENGELHARD N，ENDRES F，et al.A benchmark for the evaluation of RGB-D SLAM systems[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems，2012：573-580.