基于点面特征融合的RGB-D SLAM算法

doi:10.19678/j.issn.1000-3428.0064598

计算机工程 ›› 2023, Vol. 49 ›› Issue (6): 201-207. doi: 10.19678/j.issn.1000-3428.0064598

基于点面特征融合的RGB-D SLAM算法

孙超, 朱勇杰, 余林波, 苗隆鑫, 曹勉, 叶力, 郭乃宇

江汉大学智能制造学院, 武汉 430056

收稿日期:2022-05-02 修回日期:2022-07-24 发布日期:2023-06-10
作者简介:孙超(1984-),男,副教授、博士,主研方向为图像处理、数据处理;朱勇杰、余林波(通信作者)、苗隆鑫、曹勉、叶力、郭乃宇,硕士研究生。
基金资助:
国家重点研发计划(2018YFD1100104);湖北省重点研发计划(2020BCA084);湖北省重点培育学科控制科学与工程基金(2020XK015);江汉大学校级科研基金(2021yb153)。

RGB-D SLAM Algorithm Based on Point-Surface Feature Fusion

SUN Chao, ZHU Yongjie, YU Linbo, MIAO Longxin, CAO Mian, YE Li, GUO Naiyu

School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China

Received:2022-05-02 Revised:2022-07-24 Published:2023-06-10

摘要/Abstract

摘要： 传统的视觉SLAM算法主要依赖于点特征来构建视觉里程计，而在人造环境中通常存在一些点特征不足但结构特征丰富的场景，此时基于点特征的视觉里程计方法容易出现跟踪失败或位姿估计精度降低的问题。提出一种融合点面特征的RGB-D SLAM算法，利用人造环境中的平面特征提高SLAM算法定位与建图的精度与鲁棒性。使用AGAST特征点提取算法并采用四叉树的方式进行改进，使特征点在图像中均匀分布以减少点特征提取的冗余度。同时，在传统点特征方法的基础上添加平面特征，使用连通域分割算法从点云中获取平面特征，并构建伪平面特征，结合AGAST特征点构建点面特征融合的结构约束因子图，添加多重约束关系用于图优化。实验结果表明，该算法AGAST特征点提取效率优于ORB-SLAM2算法，融合的点面特征使其在室内环境下的定位和建图精度更高，绝对轨迹误差减小约20%，相对轨迹误差减小约10%，单帧跟踪耗时减少约7.3%。

关键词: RGB-D SLAM算法, AGAST算法, 点云分割, 点面特征融合, 图优化

Abstract: Traditional visual SLAM algorithms rely mainly on point features to construct visual odometry. There are usually scenes with insufficient point features but rich structural features in artificial environments.Currently，visual odometry methods based on point features are prone to tracking failures or reduced pose estimation accuracy.Therefore， considering the above problems，an RGB-D SLAM algorithm that fuses point and surface features is proposed，which makes full use of the plane features in an artificial environment to improve the accuracy and robustness of the SLAM algorithm positioning and mapping.The AGAST feature point extraction algorithm is used and the quadtree is used to improve it，so that the feature points are evenly distributed in the image to reduce the redundancy of point feature extraction.On the basis of the traditional point feature method，the plane feature is added，and the plane feature is obtained from the point cloud using the connected domain segmentation algorithm，and the pseudo-plane feature is constructed.Combined with the AGAST feature points，a structural constraint factor graph of point-surface feature fusion is constructed，and multiple constraint relationships are added for graph optimization. Compared with the ORB-SLAM2 algorithm，the AGAST feature point extraction efficiency of the proposed algorithm is better than that of the ORB-SLAM2 feature extraction strategy，and the fusion of point and surface features makes the positioning and mapping accuracy better in indoor environments.The dataset test results show that the absolute trajectory error of the proposed algorithm is reduced by approximately 20%，the Relative Trajectory Error（RTE） is reduced by approximately 10%， and single-frame tracking time is reduced by approximately 7.3%.

Key words: RGB-D SLAM algorithm, AGAST algorithm, point cloud segmentation, point-surface feature fusion, graph optimization

中图分类号:

TP242

孙超, 朱勇杰, 余林波, 苗隆鑫, 曹勉, 叶力, 郭乃宇. 基于点面特征融合的RGB-D SLAM算法[J]. 计算机工程, 2023, 49(6): 201-207.

SUN Chao, ZHU Yongjie, YU Linbo, MIAO Longxin, CAO Mian, YE Li, GUO Naiyu. RGB-D SLAM Algorithm Based on Point-Surface Feature Fusion[J]. Computer Engineering, 2023, 49(6): 201-207.

http://www.ecice06.com/CN/Y2023/V49/I6/201

图/表 9

20230615170019

20230615170023

20230615170027

20230615170031

20230615170034

20230615170037

20230615170040

20230615170043

20230615170047

参考文献

[1] 朱叶青,金瑞,赵良玉.大尺度弱纹理场景下多源信息融合SLAM算法[J].宇航学报,2021,42(10):1271-1282.ZHU Y Q,JIN R,ZHAO L Y.Multi-source information fusion SLAM algorithm in large-scale weak texture scenes[J].Journal of Astronautics,2021,42(10):1271-1282.(in Chinese)
[2] WANG Z M,WANG M L,YU Y L,et al.Research and implementation of ORB-SLAM in drastically changing scenes[C]//Proceedings of the 4th IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference.Washington D.C.,USA:IEEE Press,2021:1584-1587.
[3] MUR-ARTAL R,TARDÓS 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.
[4] HUANG J,LIU S.Robust simultaneous localization and mapping in low-light environment[J].Computer Animation and Virtual Worlds,2019,30(3/4):e1895.
[5] 何俊学,李战明.基于视觉的同时定位与地图构建方法综述[J].计算机应用研究,2010,27(8):2839-2844.HE J X,LI Z M.Survey of vision-based approach to simultaneous localization and mapping[J].Application Research of Computers,2010,27(8):2839-2844.(in Chinese)
[6] WEINGARTEN J,SIEGWART R.3D SLAM using planar segments[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2007:3062-3067.
[7] SALAS-MORENO R F,GLOCKEN B,KELLY P H J,et al.Dense planar SLAM[C]//Proceedings of IEEE International Symposium on Mixed and Augmented Reality.Washington D.C.,USA:IEEE Press,2014:157-164.
[8] MA L N,KERL C,STÜCKLER J,et al.CPA-SLAM:consistent plane-model alignment for direct RGB-D SLAM[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2016:1285-1291.
[9] HSIAO M,WESTMAN E,ZHANG G F,et al.Keyframe-based dense planar SLAM[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2017:5110-5117.
[10] KLEIN G,MURRAY D.Parallel tracking and mapping for small AR workspaces[C]//Proceedings of the 6th IEEE/ACM International Symposium on Mixed and Augmented Reality.Washington D.C.,USA:IEEE Press:2008:225-234.
[11] MUR-ARTAL R,MONTIEL J M M,TARDÓS J D.ORB-SLAM:a versatile and accurate monocular SLAM system[J].IEEE Transactions on Robotics,2015,31(5):1147-1163.
[12] QIN T,LI P L,SHEN S J.VINS-mono:a robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics,2018,34(4):1004-1020.
[13] DAVISON A J,REID I D,MOLTON N D,et al.MonoSLAM:real-time single camera SLAM[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1052-1067.
[14] BOLAÑOS M,DIMICCOLI M,RADEVA P.Toward storytelling from visual lifelogging:an overview[J].IEEE Transactions on Human-Machine Systems,2017,47(1):77-90.
[15] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[16] BAY H,TUYTELAARS T,VAN GOOL L.SURF:speeded up robust features[C]//Proceedings of the 9th European Conference on Computer Vision.New York,USA:ACM Press,2006:404-417.
[17] 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.
[18] MAIR E,HAGER G D,BURSCHKA D,et al.Adaptive and generic corner detection based on the accelerated segment test[C]//Proceedings of the 11th European Conference on Computer Vision.New York,USA:ACM Press,2010:183-196.
[19] TREVOR A J B,GEDIKLI S,RUSU R B,et al.Efficient organized point cloud segmentation with connected components[C]//Proceedings of SPME'13.Washington D.C.,USA:IEEE Press,2013:1-10.
[20] FENG C,TAGUCHI Y,KAMAT V R.Fast plane extraction in organized point clouds using agglomerative hierarchical clustering[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2014:6218-6225.
[21] ZHANG X Y,WANG W,QI X Y,et al.Point-plane SLAM using supposed planes for indoor environments[J].Sensors(Basel,Switzerland),2019,19(17):3795.
[22] FISCHLER M A,BOLLES R C.Random sample consensus[J].Communications of the ACM,1981,24(6):381-395.
[23] LIU Y J,CHEN T,CHEN X Y,et al.Planar shape matching and feature extraction using shape profile[C]//Proceedings of International Conference on Geometric Modeling and Processing.Berlin,Germany:Springer,2008:358-369.
[24] KÜMMERLE R,GRISETTI G,STRASDAT H,et al.g2o:a general framework for graph optimization[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2011:3607-3613.
[25] NEWMAN P,HO K.SLAM-loop closing with visually salient features[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2006:635-642.

选择文件类型/文献管理软件名称

选择包含的内容

基于点面特征融合的RGB-D SLAM算法

RGB-D SLAM Algorithm Based on Point-Surface Feature Fusion

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	王志江, 秦品乐, 柴锐, 武峰, 程一彤, 史玥. 基于深度学习的牙齿嵌塞自动判别方法[J]. 计算机工程, 2022, 48(4): 307-313.
[2]	曾碧,黄文. 一种融合多特征聚类集成的室内点云分割方法[J]. 计算机工程, 2018, 44(3): 281-286.
[3]	袁小翠,陈华伟. 点云模型特征面分割与识别方法[J]. 计算机工程, 2018, 44(11): 245-250.
[4]	吕瑞,李明,汪明阔,刘欢欢,薛静远. 一种融合多帧ICP 和图优化的算法研究[J]. 计算机工程, 2014, 40(9): 229-232.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于点面特征融合的RGB-D SLAM算法

RGB-D SLAM Algorithm Based on Point-Surface Feature Fusion

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献

相关文章 4

编辑推荐

Metrics

本文评价