Motion estimation of indoor robot based on image sequences and improved particle filter
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Robot motion estimation is fundamental in most robot applications such as robot navigation, which is an indispensable part of future internet of things. Indoor robot motion estimation is difficult to be resolved because GPS (Global Positioning System) is unavailable. Vision sensors can provide larger amount of image sequences information compared with other traditional sensors, but it is subject to the changes of light. In order to improve the robustness of indoor robot motion estimation, an enhanced particle filter framework is constructed: firstly, motion estimation was implemented based on the distinguished indoor feature points; secondly, particle filter method was utilized and the least square curve fitting was inserted into the particle resampling process to solve the problem of particle depletion. The various experiments based on real robots show that the proposed method can reduce the estimation errors greatly and provide an effective resolution for the indoor robot localization and motion estimation.
KeywordsInternet of things Visual odometry Indoor robot Localization Motion estimation
The paper was supported by National Natural Science Foundation of China (Nos. 61763048,61263022, 61303234), National Social Science Foundation of China (No. 12XTQ012), Science and Technology Foundation of Yunnan Province (Nos. 2017FB095, 201801PF00021), the 18th Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Personnel Training Program (No.2015HB038). It is also supported by the Foundation of University Research and Innovation Platform Team for Intelligent Perception and Computing of Anhui Province, key research project of natural science of Anhui Provincial Education Department (KJ2017A354). Anhui Provincial Natural Science Foundation of China (1608085MF144). The authors would like to thank the anonymous reviewers and the editors for their suggestions.
Rong Jiang, Xiaoming Dong designed the experiments and wrote the paper; Rong Jiang, Xiaoming Dong performed the experiments; Liefu Ai analyzed the data; All authors have read and approved the final manuscript.
Compliance with ethical standards
The authors declare that they have no competing interests.
- 1.Alon J, Sclaroff S (2000) Recursive estimation of motion and planar structure. In: IEEE computer vision and pattern recognition, pages II:550–556Google Scholar
- 2.Ascani A, Frontoni E, Mancini A 2008 Feature group matching for appearance-based localization. In: IROSGoogle Scholar
- 6.Campbell J, Sukthankar R, Nourbakhsh I (2004) Techniques for evaluating optical flow in extreme terrain. Proceedings of IROSGoogle Scholar
- 8.Cumani A, Guiducci A (2009) ,Comparison of feature detectors for rover navigation. MMCTEE pp.126-131. Google Scholar
- 10.Eckenhoff K, Paull L, Huang G (2016) Decoupled, consistent node removal and edge sparsification for graph-based SLAM[C]. IEEE international conference on intelligent robots and systems pp 3275–3282Google Scholar
- 11.Helmick DM, Chang Y, Roumeliotis SI, Clouse D, Matthies L (2004) Path following using visual odometry for a mars rover in high-slip environments. In: Proc. 2004 IEEE Aerospace Conference, Big Sky, MT,Mar. 6–13, 2004Google Scholar
- 16.Montemerlo M, Thrun S, Koller D et al (2002) FastSLAM: a factored solution to the simultaneous localization and mapping problem[C], AAAI pp 593–598Google Scholar
- 19.Potkonjak M, Feng J, Girod L (2006) Location discovery using data drive statistical error modeling[J]. IEEE FOCOM 26(8):43–47Google Scholar
- 20.Sajeeb R, Manohar CS, Roy D (2009) Rao-Blackwellization with substructuring for state and parameter estimations of a class of nonlinear dynamical systems. IJEUU 1:1–2Google Scholar
- 21.Sirtkaya S, Seymen B, Alatan AA (2013) Loosely coupled Kalman filtering for fusion of visual odometry and inertial navigation. In: Proceedings of the 2013 16th international conference on information fusion (FUSION), Istanbul, Turkey, 9–12 July 2013, pp 219–226Google Scholar
- 24.Wang H, Yuan K, Zou W, Zhou Q (2005) Visual odometry based on locally planar ground assumption, IEEE International Conference on Information Acquisition(ICIA2005)Google Scholar
- 25.Wang C, Wang T, Liang J, Chen Y, Wu Y (2012) Monocular vision and IMU based navigation for a small unmanned helicopter. In: Proceedings of the 2012 7th IEEE conference on Industrial Electronics and Applications (ICIEA), Singapore, 18–20 July 2012, pp 1694–1699Google Scholar