IMU-assisted Tracking Model of Monocular Vision Inertial Positioning

doi:10.13474/j.cnki.11-2246.2018.0350

Abstract

Abstract:

In order to solve the outstanding problem of poor monocular vision inertial positioning accuracy under violent motion,an IMU-assisted tracking model for monocular vision inertial tightly-coupled positioning is proposed to improve the robustness under monocular vision inertial tightly-coupled positioning.The IMU-assisted tracking model is used to replace the conventional reference frame and constant speed tracking model.The model is divided into two phases.In the initialization phase,on the basis of setting the initial translation of the current frame in the constant model,the IMU pre-integration is used to determine the initial rotation of the current frame so as to obtain the initial pose of the current frame.After the initialization is completed,the prior velocity information of the IMU pre-integration is added based on the initial pose provided.Finally,the tracking model is established from the initial state calculated above to achieve accurate positioning.Validation uses open indoor SLAM data sets.The results show that the proposed IMU-assisted tracking model can effectively improve the robustness of the system,while the positioning accuracy is controlled in about 0.1 m,the accuracy is about 20% higher than that of the traditional tracking model.

Key words: monocular visual inertia, IMU-assisted tracking model, initial state, robustness

CLC Number:

WANG Shuai, PAN Shuguo, HUANG Lixiao, ZENG Pan. IMU-assisted Tracking Model of Monocular Vision Inertial Positioning[J]. 测绘通报, 2018, 0(11): 58-62.

References

[1] 胡劲草.室内自主式移动机器人定位方法研究[J].传感器世界,2006,20(11):14-18.
[2] 李群明,熊蓉,褚健.室内自主移动机器人定位方法研究综述[J].机器人,2003,25(6):560-567.
[3] PAN S G,MENG X,WANG S L,et al.Ambiguity Resolution with Double Troposphere Parameter Restriction for Long Range Reference Stations in NRTK System[J].Empire Survey Review,2015,47(345):429-437.
[4] PAN S,MENG X,WANG G,et al.A New Approach for Optimising GNSS Positioning Performance in Harsh Observation Environments[J].Journal of Navigation,2014,67(6):1029-1048.
[5] 姜卫平.卫星导航定位基准站网的发展现状、机遇与挑战[J].测绘学报,2017,46(10):1379-1388.
[6] 王勇,陈国良,李晓园,等.一种相机标定辅助的单目视觉室内定位方法[J].测绘通报,2018(2):35-40.
[7] JULIER S J,UHLMANN J K.A New Extension of the Kalman Filter to Nonlinear Systems[C]//Proceedings of SPIE.[S.l.]:SPIE,1997.
[8] TRIGGS B,MCLAUCHLAN P F,HARTLEY R I,et al.Bundle Adjustment-A Modern Synthesis[C]//International Workshop on Vision Algorithms.Berlin:Springer,1999:298-372.
[9] MOURIKIS A I,ROUMELIOTIS S I.A Multi-state Constraint Kalman Filter for Vision-aided Inertial Navigation[C]//Proceedings 2007 IEEE International Conference on Robotics and Automation. Roma:IEEE,2007:3565-3572.
[10] 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.
[11] MUR-ARTAL R,TARDÓS J D.Visual-inertial Monocular SLAM with Map Reuse[J].IEEE Robotics and Automation Letters,2017,2(2):796-803.
[12] LEE D,KIM H,MYUNG H.2D Image Feature-Based Real-Time RGB-D 3D SLAM[J].Springer Berlin Heidelberg,2013,208:485-492.
[13] QIN T,LI P,SHEN S.Vins-mono:A Robust and Versatile Monocular Visual-inertial State Estimator[J].ArXiv Preprint,2017,8:210-235.
[14] FORSTER C,CARLONE L,DELLAERT F,et al.On-manifold Preintegration for Real-time Visual-inertial Odometry[J].IEEE Transactions on Robotics,2017,33(1):1-21.
[15] LEUTENEGGER S,LYNEN S,BOSSE M,et al.Keyframe-based Visual-inertial Odometry Using Nonlinear Optimization[J].The International Journal of Robotics Research,2015,34(3):314-334.