Evaluation of SLAM Algorithms for Highly Dynamic Environments
Abstract
Simultaneous Localization And Mapping (SLAM) has received considerably attention in the mobile robotics community for more than 25 years. Most SLAM algorithms have been developed for and successfully tested in static environments. Previous studies that investigated the use of SLAM algorithms in dynamic environments only considered partially dynamic environment in which only a few objects are non-static. In this paper, we evaluate several popular SLAM algorithms for use in highly dynamic environments in which all objects are only temporarily static, i.e. all objects will be moved within a short time frame. To this end, we built a static test environment and defined two different scenarios based on a warehouse environment to simulate highly dynamic environments. Four different 2D SLAM algorithms that are available in Robotic Operating System (ROS) are employed and evaluated through visual inspection of produced maps and the difference between the object positions in obtained maps and their real positions in the environment. Based on our conducted evaluation Hector Mapping achieves the best performance in both scenarios.
Keywords
SLAM ROS Highly dynamic environments Benchmarking TurtleBot3 BurgerReferences
- 1.Bahraini, M.S., Bozorg, M., Rad, A.B.: SLAM in dynamic environments via ML-RANSAC. Mechatronics 49, 105–118 (2018)CrossRefGoogle Scholar
- 2.Bresson, G., Alsayed, Z., Yu, L., Glaser, S.: Simultaneous localization and mapping: a survey of current trends in autonomous driving. IEEE Trans. Intell. Veh. (T-IV) 2(3), 194–220 (2017)CrossRefGoogle Scholar
- 3.Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309–1332 (2016)CrossRefGoogle Scholar
- 4.Fink, G., Franke, M., Lynch, A.F., Röbenack, K., Godbolt, B.: Visual inertial slam: application to unmanned aerial vehicles. IFAC-PapersOnLine 50(1), 1965–1970 (2017)CrossRefGoogle Scholar
- 5.Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, April 2005Google Scholar
- 6.Hidalgo, F., Bräunl, T.: Review of underwater slam techniques. In: 6th International Conference on Automation, Robotics and Applications (ICARA), Queenstown, New Zealand, April 2015Google Scholar
- 7.Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable slam system with full 3D motion estimation. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Kyoto, Japan, November 2011Google Scholar
- 8.Konolige, K., Grisetti, G., Kümmerle, R., Burgard, W., Limketkai, B., Vincent, R.: Efficient sparse pose adjustment for 2D mapping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010Google Scholar
- 9.Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robot. 4(4), 333–349 (1997)CrossRefGoogle Scholar
- 10.Moravec, H.P., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), March 1985Google Scholar
- 11.Santos, J.M., Portugal, D., Rocha, R.P.: An evaluation of 2D SLAM techniques available in robot operating system. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Linkoping, Sweden, January 2014Google Scholar
- 12.Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics. Springer, Heidelberg (2008)zbMATHGoogle Scholar
- 13.Sun, Y., Liu, M., Meng, M.Q.H.: Improving RGB-D SLAM in dynamic environments: a motion removal approach. Robot. Auton. Syst. 89, 110–122 (2017)CrossRefGoogle Scholar
- 14.Tan, W., Liu, H., Dong, Z., Zhang, G., Bao, H.: Robust monocular SLAM in dynamic environments. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Adelaide, Australia, December 2013Google Scholar
- 15.Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. The MIT Press, Cambridge (2005)zbMATHGoogle Scholar
- 16.Vincent, R., Limketkai, B., Eriksen, M.: Comparison of indoor robot localization techniques in the absence of GPS. In: Proceedings of Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, vol. 7664, April 2010Google Scholar
- 17.Wolf, D.F., Sukhatme, G.S.: Mobile robot simultaneous localization and mapping in dynamic environments. Auton. Robot. 19(1), 53–65 (2005)CrossRefGoogle Scholar
- 18.Xiang, L., Ren, Z., Ni, M., Jenkins, O.C.: Robust graph SLAM in dynamic environments with moving landmarks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, September-October 2015Google Scholar
- 19.Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Monterey, CA, USA, July 1997Google Scholar
- 20.Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proceedings of the Second International Conference on Autonomous Agents, Minneapolis, Minnesota, USA, pp. 47–53, May 1998Google Scholar
- 21.Zou, D., Tan, P.: CoSLAM: collaborative visual slam in dynamic environments. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 354–366 (2013)CrossRefGoogle Scholar