Abstract
Visual cameras and encoders are usually equipped on mobile robotic systems. In this paper, we present a robust extended Kalman filter-based pose estimation approach by fusing the information from both the onboard camera and encoders. Different from existing works, the system state is chosen in a new simplified way, including the robot pose and the depth of feature points. Moreover, a new observation model is formulated and the corresponding Jacobian matrix is derived. A robust feature association approach with an outlier removing mechanism is proposed. Experimental results are provided to demonstrate the effectiveness of the proposed approach.
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
Chen S (2012) Kalman filter for robot vision: a survey. IEEE Trans Ind Electron 59(11):4409–4420
He W, Fang Y, Zhang X (2013) Prediction-based interception control strategy design with a specified approach angle constraint for wheeled service robots. In: Proceedings of 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS). Tokyo, Japan, pp 2725–2730
Hesch JA, Bowman DG, Kottasand SL, Roumeliotis SI (2014) Consistency analysis and improvement of vision-aided inertial navigation. IEEE Trans Rob 30(1):158–176
Lategahn H, Geiger A, Kitt B (2011) Visual SLAM for autonomous ground vehicles. In: Proceedings of IEEE international conference on robotics and automation. Shanghai, China, pp 1732–1737
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Lui V, Drummond T (2015) Image based optimization without global consistency for constant time monocular visual SLAM. In: Proceedings of 2015 IEEE international conference on robotics and automation (ICRA). Seattle, Washington, pp 5799–5806
Martinelli A (2012) Vision and IMU data fusion: closed-form solutions for attitude, speed, absolute scale, and bias determination. IEEE Trans Rob 28(1):44–60
McDonald J, Kaess M, Cadena C, Neira J, Leonard JJ (2013) Real-time 6-DOF multi-session visual SLAM over large-scale environments. Robot Auton Syst 61(10):1144–1158
Naroditsky O, Zhou XS, Gallier J, Roumeliotis SI, Daniilidis K (2012) Two efficient solutions for visual odometry using directional correspondence. IEEE Trans Pattern Anal Mach Intell 34(4):818–824
Panahandeh G, Jansson M (2014) Vision-aided inertial navigation based on ground plane feature detection. IEEE/ASME Trans Mechatron 19(4):1206–1215
Scaramuzza D, Fraundorfer F (2011) Visual odometry, part I: the first 30 years and fundamentals. IEEE Robot Autom Mag 18(4):80–92
Spica R, Giordano PR, Chaumette F (2014) Active structure from motion: application to point, sphere, and cylinder. IEEE Trans Rob 30(6):1499–1513
Zhang X, Fang Y, Liu X (2011) Motion-estimation-based visual servoing of nonholonomic mobile robots. IEEE Trans Rob 27(6):1167–1175
Zhang X, Fang Y, Sun N (2015) Visual servoing of mobile robots for posture stabilization: from theory to experiments. Int J Robust Nonlinear Control 25(1):1–15
Zhang X, Wang C, Fang Y, Xing K (2014) Planar motion estimation from three-dimensional scenes. In: Proceedings of the 2014 IROS workshop on visual control of mobile robots, pp 21–26
Acknowledgments
Project supported in part by the National Natural Science Foundation of China (No. 61203333 and No. 61202203), in part by Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20120031120040).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Zhejiang University Press and Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhang, Xb., Wang, Cy., Fang, Yc., Xing, Kx. (2017). An Extended Kalman Filter-Based Robot Pose Estimation Approach with Vision and Odometry. In: Yang, C., Virk, G., Yang, H. (eds) Wearable Sensors and Robots. Lecture Notes in Electrical Engineering, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-10-2404-7_41
Download citation
DOI: https://doi.org/10.1007/978-981-10-2404-7_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2403-0
Online ISBN: 978-981-10-2404-7
eBook Packages: EngineeringEngineering (R0)