Pose Estimation via Complementary Filtering of Markers and Odometry
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13724Keywords:
Filtering and smoothing, Estimation and filtering, Robot perception and sensing, Robust estimation, Human machine cooperation & integrationAbstract
Accurate estimation of rigid object pose is a fundamental requirement in vision-based robotic. In eye-in-hand configurations, estimates obtained through Perspective-n-Point (PnP) from visual markers exhibit frame-to-frame noise.
We propose a recursive method that combines visual observations of ArUco markers with the robot odometry. The odometry transform the observations into the robot base coordinate frame, where the object remains static, and a robust exponential weighted moving average (EWMA) formulated on the Lie group SE(3) is applied to them. The method incorporates a state-dependent Huber weighting term and a reprojection error-based weight from PnP to model the reliability of each observation.
Experimental validation is carried out using a robotic arm with a monocular camera observing a static object with ArUco markers. The results show a significant reduction in relative pose error and temporal jitter compared to direct PnP and EWMA variants without robust weighting.
References
Chaumette, F., Hutchinson, S., 2006. Visual servo control. i. basic approaches. IEEE Robotics & Automation Magazine 13 (4), 82–90. DOI: 10.1109/MRA.2006.250573
García-Ruiz, P., Romero-Ramirez, F. J., Muñoz-Salinas, R., Marín-Jiménez, M. J., Medina-Carnicer, R., 2023. Fiducial objects: Custom design and evaluation. Sensors 23 (24). DOI: 10.3390/s23249649
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M., 2014. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition 47 (6), 2280–2292. DOI: 10.1016/j.patcog.2014.01.005
Goodrich, M. A., Schultz, A. C., Jan. 2007. Human-robot interaction: a survey. Found. Trends Hum.-Comput. Interact. 1 (3), 203–275. DOI: 10.1561/1100000005
Hartley, R., Trumpf, J., Dai, Y., Li, H., 2013. Rotation averaging. International Journal of Computer Vision 103 (3), 267–305. DOI: 10.1007/s11263-012-0601-0
Huber, P. J., 1964. Robust estimation of a location parameter. The Annals of Mathematical Statistics 35 (1), 73–101. DOI: 10.1214/aoms/1177703732
Kam, H. C., Yu, Y. K., Wong, K. H., 2018. An improvement on ArUco marker for pose tracking using kalman filter. In: 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). pp. 65–69. DOI: 10.1109/SNPD.2018.8441049
Karcher, H., 1977. Riemannian center of mass and mollifier smoothing. Communications on Pure and Applied Mathematics 30 (5), 509–541. DOI: 10.1002/cpa.3160300502
Kim, D., Bong, J. H., Jeong, S., 2024. Enhancing pose estimation using multiple graphical markers with spatial and temporal outlier detection. Applied Sciences 14 (22). DOI: 10.3390/app142210225
Labbé, Y., Manuelli, L., Mousavian, A., Tyree, S., Birchfield, S., Tremblay, J., Carpentier, J., Aubry, M., Fox, D., Sivic, J., 2022. Megapose: 6d pose estimation of novel objects via render & compare. In: Proceedings of the 6th Conference on Robot Learning (CoRL).
Lepetit, V., Moreno-Noguer, F., Fua, P., Feb. 2009. Epnp: An accurate o(n) solution to the pnp problem. Int. J. Comput. Vision 81 (2), 155–166. DOI: 10.1007/s11263-008-0152-6
Marchand, E., Uchiyama, H., Spindler, F., 2016. Pose estimation for augmented reality: A hands-on survey. IEEE Transactions on Visualization and Computer Graphics 22 (12), 2633–2651. DOI: 10.1109/TVCG.2015.2513408
Solà, J., Deray, J., Atchuthan, D., 2018. A micro Lie theory for state estimation in robotics. ArXiv:1812.01537.
Terzakis, G., Lourakis, M., 2020. A consistently fast and globally optimal solution to the perspective-n-point problem. In: ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I. Springer-Verlag, pp. 478–494. DOI: 10.1007/978-3-030-58452-8 28
Tsai, R., Lenz, R., 1989. A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Transactions on Robotics and Automation 5 (3), 345–358. DOI: 10.1109/70.34770
Zhang, Z., Scaramuzza, D., 2018. A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 7244–7251. DOI: 10.1109/IROS.2018.8593941
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Copyright (c) 2026 Victor Quesada Conejero, Fran Real, Sergio Garrido-Jurado
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