Abstract
The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download http://babel.isa.uma.es/mrpt/papers/dataset2009/.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256.
Blanco, J. L. (2008). The Mobile Robot Programming Toolkit (MRPT) website.
Bonarini, A., Migliore, D., Fontana, G., & Matteucci, M. (2009). The Raw Seeds project website.
Borkowski, K. M. (1987). Transformation of geocentric to geodetic coordinates without approximations. Astrophysics and Space Science, 139(1), 1–4.
Campbell, J., Sukthankar, R., Nourbakhsh, I., & Pahwa, A. (2005). A robust visual odometry and precipice detection system using consumer-grade monocular vision. In IEEE international conference on robotics and automation (pp. 3421–3427).
Civera, J., Davison, A. J. & Montiel, J. M. M. (2008). Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics, 24(5).
Davison, A. J., Reid, I., Molton, N., & Stasse, O. (2007). MonoSLAM: real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6).
De Boor, C. (2001). A practical guide to splines. Berlin: Springer.
Drake, S. P. (2000). Converting GPS coordinates λ φ h to local coordinates enu.
Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: part I. IEEE Robotics and Automation Magazine, 13(2), 99–110.
Guivant, J. E., & Nebot, E. M. (2001). Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Transactions on Robotics and Automation, 17(3), 242–257.
Guivant, J., Nebot, E., Nieto, J., & Masson, F. (2004). Navigation and mapping in large unstructured environments. International Journal of Robotics Research, 23(4), 449–472.
Horn, B. K. P. (1987). Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A, 4(4), 629–642.
Howard, A., & Roy, N. (2003). The robotics data set repository (radish).
Kaess, M., Ranganathan, A., & Dellaert, F. (2007). Fast incremental square root information smoothing. In International joint conferences on artificial intelligence (IJCAI) (pp. 2129–2134).
Leick, A. (2004). GPS satellite surveying. New York: Wiley.
Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 11(2), 431–441.
Montemerlo, M. (2003). FastSLAM: a factored solution to the simultaneous localization and mapping problem with unknown data association. PhD thesis, University of Washington.
Mor, J. J. (1977). The Levenberg-Marquardt algorithm: implementation and theory. In Lecture notes in mathematics (Vol. 630, pp. 105–116). Berlin: Springer.
Nieto, J., Guivant, J., Nebot, E., & Thrun, S. (2003). Real time data association for FastSLAM. In IEEE/RSJ international conference on intelligent robots and systems (Vol. 1).
Paz, L.M., Guivant, J., Tardós, J. D., & Neira, J. (2007). Data association in O(n) for divide and conquer SLAM. In Robotics: science and systems, RSS, Atlanta, GA, USA, June 2007.
Smith, R., Self, M., & Cheeseman, P. (1988). A stochastic map for uncertain spatial relationships. In The fourth international symposium on robotics research (pp. 467–474).
Walter, M., Eustice, R., & Leonard, J. (2004). A provably consistent method for imposing sparsity in feature-based SLAM information filters. In International symposium of robotics research (ISRR). Berlin: Springer.
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work has been partly supported by the Spanish Government under research contract DPI2005-01391 and the FPU grant program.
Rights and permissions
About this article
Cite this article
Blanco, JL., Moreno, FA. & Gonzalez, J. A collection of outdoor robotic datasets with centimeter-accuracy ground truth. Auton Robot 27, 327–351 (2009). https://doi.org/10.1007/s10514-009-9138-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-009-9138-7