Abstract
In the process of mobile robot SLAM based on monocular vision, the data association between the landmark and the imaging feature point is vital to localization accurately. RANSAC can remove mismatching points, and estimate localization robustly. But it chooses the correct model by random sample without efficient, and doesn’t distinct between inlier points for localization, which decreases localization accuracy. For the above questions, a fast and high accuracy localization estimation algorithm is proposed. A approximate localization model is predicted with the KF filter, which accelerates initial correct localization model selection. The consistent set is obtained based on geometric distance threshold, and variance weighted BA optimization is used to improve the accuracy of SLAM localization estimation. The numerical simulations show that the proposed localization estimation method is effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee, T., Kim, C., Cho, D.D.: A monocular vision sensor-based efficient SLAM method for indoor service robots. IEEE Trans. Ind. Electron. 66(1), 318–328 (2018)
Yu, H., Ji, N., Ren, Y., et al.: A special event-based K-nearest neighbor model for short-term traffic state prediction. IEEE Access 7, 81717–81729 (2019)
Kaess, M., Dellaert, F.: Covariance recovery from a square root information matrix for data association. Robot. Auton. Syst. 57(12), 1198–1210 (2009)
Hossein-Nejad, Z., Nasri, M.: A-RANSAC: adaptive random sample consensus method in multimodal retinal image registration. Biomed. Signal Process. Control 45, 325–338 (2018)
Hossein-Nejad, Z., Nasri, M.: An adaptive image registration method based on SIFT features and RANSAC transform. Comput. Electr. Eng. 62, 524–537 (2017)
Li, Y., Fan, S., Sun, Y., et al.: Bundle adjustment method using sparse BFGS solution. Remote Sens. Lett. 9(8), 789–798 (2018)
Ovechkin, V., Indelman, V.: BAFS: bundle adjustment with feature scale constraints for enhanced estimation accuracy. IEEE Robot. Autom. Lett. 3(2), 804–810 (2018)
Karrer, M., Schmuck, P., Chli, M.: CVI-SLAM—collaborative visual-inertial SLAM. IEEE Robot. Autom. Lett. 3(4), 2762–2769 (2018)
Ruchanurucks, M., Rakprayoon, P., Kongkaew, S.: Automatic landing assist system using IMU + PnP for robust positioning of fixed-wing UAVs. J. Intell. Robot. Syst. 90(1–2), 189–199 (2018)
Morgan, I., Jayarathne, U., Rankin, A., et al.: Hand-eye calibration for surgical cameras: a procrustean perspective-n-point solution. Int. J. Comput. Assist. Radiol. Surg. 12(7), 1141–1149 (2017)
Acknowledgments
This work was supported by Shaanxi Province Key Research and Development program (Program No. 2018GY-184), and supported by the Program for Innovative Science and Research Team of Xi’an Technological University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, J., Han, B., Yan, K. (2020). A Fast and High Accuracy Localization Estimation Algorithm Based on Monocular Vision. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2019. Advances in Intelligent Systems and Computing, vol 1117. Springer, Singapore. https://doi.org/10.1007/978-981-15-2568-1_33
Download citation
DOI: https://doi.org/10.1007/978-981-15-2568-1_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2567-4
Online ISBN: 978-981-15-2568-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)