Abstract
Precise motion estimation is vital for any mobile robot to correctly control its actuators and thus to navigate through terrain. Basic approaches of motion estimation (e.g. wheel odometry) that can be considered reliable in laboratory conditions tend to fail in real-world search and rescue scenarios because of uneven and slippery surface the robot has to cross. In this article, we pick some of the current localization and motion estimation techniques and discuss their prerequisites in contrast with experience gathered during end-user evaluations and a real-world deployment of our robotic platform in a town struck by an earthquake (Mirandola, Italy). The robotic platform is equipped with a set of sensors allowing us to combine various approaches to robot localization and motion estimation in order to increase the redundancy in the system and thus the overall reliability. We present our approach to fuse selected sensor modalities that was developed with emphasis on possible sensor failures, which have been subsequently experimentally tested.
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
Preview
Unable to display preview. Download preview PDF.
References
Anousaki, G., Kyriakopoulos, K.J.: A dead-reckoning scheme for skid-steered vehicles in outdoor environments. In: Proc. IEEE Int. Conf. Robotics and Automation ICRA 2004, vol. 1, pp. 580–585 (2004)
Besl, P., McKay, H.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Chiu, H.-P., Williams, S., Dellaert, F., Samarasekera, S., Kumar, R.: Robust vision-aided navigation using sliding-window factor graphs. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 46–53 (2013)
Divis, J.: Visual odometry from omnidirectional camera. Master’s thesis, Charles university in Prague, Faculty of mathematics and physics (2012)
Endo, D., Okada, Y., Nagatani, K., Yoshida, K.: Path following control for tracked vehicles based on slip-compensating odometry. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems IROS 2007, pp. 2871–2876 (2007)
Fraundorfer, F., Scaramuzza, D.: Visual odometry: Part ii: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine 19(2), 78–90 (2012)
Hoang, V.-D., Hernandez, D.C., Le, M.-H., Jo, K.-H.: 3d motion estimation based on pitch and azimuth from respective camera and laser rangefinder sensing. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 735–740 (2013)
Kelly, J., Sukhatme, G.S.: Visual-inertial sensor fusion: Localization, mapping and sensor-to-sensor self-calibration. The International Journal of Robotics Research 30(1), 56–79 (2011)
Kohlbrecher, S., Von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable slam system with full 3d motion estimation. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 155–160 (2011)
Kubelka, V., Oswald, L., Pomerleau, F., Colas, F., Svoboda, T., Reinstein, M.: Robust data fusion of multimodal sensory information for mobile robots. Journal of Field Robotics (early access, 2014)
Kubelka, V., Reinstein, M.: Complementary filtering approach to orientation estimation using inertial sensors only. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 599–605 (2012)
Nagatani, K., Okada, Y., Tokunaga, N., Kiribayashi, S., Yoshida, K., Ohno, K., Takeuchi, E., Tadokoro, S., Akiyama, H., Noda, I., Yoshida, T., Koyanagi, E.: Multirobot exploration for search and rescue missions: A report on map building in robocuprescue 2009. Journal of Field Robotics 28(3), 373–387 (2011)
Nuchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM - 3D mapping outdoor environments. Journal of Field Robotics 24(8-9), 699–722 (2007)
Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing icp variants on real-world data sets. Autonomous Robots 34(3), 133–148 (2013)
Reinstein, M., Hoffmann, M.: Dead reckoning in a dynamic quadruped robot based on multimodal proprioceptive sensory information. IEEE Transactions on Robotics 29(2), 563–571 (2013)
Reinstein, M., Kubelka, V., Zimmermann, K.: Terrain adaptive odometry for mobile skid-steer robots. In: Proc. IEEE Int. Robotics and Automation (ICRA) Conf., pp. 4706–4711 (2013)
Rohac, J., Reinstein, M., Draxler, K.: Data processing of inertial sensors in strong-vibration environment. In: 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 1, pp. 71–75 (September 2011)
Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Robot Autom. Mag. 18(4), 80–92 (2011)
Simanek, J., Reinstein, M., Kubelka, V.: Evaluation of the ekf-based estimation architectures for data fusion in mobile robots. IEEE/ASME Transactions on Mechatronics, 1–6 (early access, 2014)
Sukumar, S.R., Bozdogan, H., Page, D.L., Koschan, A.F., Abidi, M.A.: Sensor selection using information complexity for multi-sensor mobile robot localization. In: 2007 IEEE International Conference on Robotics and Automation, pp. 4158–4163 (2007)
Tipaldi, G.D., Meyer-Delius, D., Burgard, W.: Lifelong localization in changing environments. The International Journal of Robotics Research 32(14), 1662–1678 (2013)
Weiss, S., Siegwart, R.: Real-time metric state estimation for modular vision-inertial systems. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4531–4537 (2011)
Yi, J., Zhang, J., Song, D., Jayasuriya, S.: Imu-based localization and slip estimation for skid-steered mobile robots. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems IROS 2007, pp. 2845–2850 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kubelka, V., Reinstein, M. (2014). Combining Complementary Motion Estimation Approaches to Increase Reliability in Urban Search & Rescue Missions. In: Hodicky, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2014. Lecture Notes in Computer Science, vol 8906. Springer, Cham. https://doi.org/10.1007/978-3-319-13823-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-13823-7_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13822-0
Online ISBN: 978-3-319-13823-7
eBook Packages: Computer ScienceComputer Science (R0)