Abstract
Many applications for robotic systems require the systems to traverse diverse, unstructured environments. State estimation with Visual Odometry (VO) in these applications is challenging because there is no single algorithm that performs well across all environments and situations. The unique trade-offs inherent to each algorithm mean different algorithms excel in different environments. We develop a method to increase robustness in state estimation by using an ensemble of VO algorithms. The method combines the estimates by dynamically switching to the best algorithm for the current context, according to a statistical model of VO estimate errors. The model is a Random Forest regressor that is trained to predict the accuracy of each algorithm as a function of different features extracted from the sensory input. We evaluate our method in a dataset of consisting of four unique environments and eight runs, totaling over 25 min of data. Our method reduces the mean translational relative pose error by 3.5 % and the angular error by 4.3 % compared to the single best odometry algorithm. Compared to the poorest performing odometry algorithm, our method reduces the mean translational error by 39.4 % and the angular error by 20.1 %.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE TPAMI 14(2), 239–256 (1992)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: ICML, pp. 161–168 (2006)
Censi, A.: An accurate closed-form estimate of ICP’s covariance. In: ICRA, pp. 3167–3172 (2007)
Fang, Z., Scherer, S.: Experimental study of odometry estimation methods using RGB-D cameras. In: IROS, pp. 680–687 (2014)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Honegger, D., Meier, L., Tanskanen, P., Pollefeys, M.: An open source and open hardware embedded metric optical flow cmos camera for indoor and outdoor applications. In: ICRA, pp. 1736–1741 (2013)
Huang, A.S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., Roy, N.: Visual odometry and mapping for autonomous flight using an RGB-D camera. In: International Symposium on Robotics Research (ISRR), pp. 1–16 (2011)
Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008)
Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME J. Basic Eng. (1960)
Lalonde, J.F., Vandapel, N., Huber, D.F., Hebert, M.: Natural terrain classification using three-dimensional ladar data for ground robot mobility. J. Field Robot. 23(10), 839–861 (2006)
Leishman, R.C., Koch, D.P., McLain, T.W., Beard, R.W.: Robust visual motion estimation using RGB-D cameras. In: AIAA Infotech Aerospace Conference, pp. 1–13 (2013)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IROS, pp. 573–580. IEEE (2012)
Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I.L., Ruess, F., Suppa, M., Burschka, D.: Toward a fully autonomous UAV: research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)
Vega-Brown, W., Bachrach, A., Bry, A., Kelly, J., Roy, N.: CELLO: a fast algorithm for covariance estimation. In: ICRA, pp. 3160–3167 (2013)
Zhang, Y., Chamseddine, A., Rabbath, C., Gordon, B., Su, C.Y., Rakheja, S., Fulford, C., Apkarian, J., Gosselin, P.: Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed. J. Franklin Inst. 350(9), 2396–2422 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Holtz, K., Maturana, D., Scherer, S. (2016). Learning a Context-Dependent Switching Strategy for Robust Visual Odometry. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-27702-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27700-4
Online ISBN: 978-3-319-27702-8
eBook Packages: EngineeringEngineering (R0)