Abstract
Stable and robust path planning and movement in ground mobile robots require a combination of accuracy and low latency in their state estimation. However, state estimation algorithms must provide these qualities under the computational and power constraints of embedded hardware. Simultaneous localization and map** (SLAM) algorithms are the best choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. Moreover, a single-camera setup is the most common solution for robotic platforms, which reduces our domain of interest to the specific SLAM algorithms type—monocular SLAM. Yet, it is still not clear from the existing literature, which monocular SLAM algorithms perform well under the accuracy, latency, and computational constraints of a ground mobile robot with onboard state estimation. This paper evaluates an array of the most recent publicly available monocular SLAM methods: ORB-SLAM2, DSO, and LDSO. The evaluation considers the pose estimation accuracy (alignment error, absolute trajectory error, and relative pose error) while processing the TUM Mono and EuRoC datasets on the specific hardware platform with a balanced amount of computational resources and power consumption. We present our complete results as a benchmark for the research community.
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References
Dissanayake, M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)
Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017)
Delmerico, J., Scaramuzza, D.: A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots. In: IEEE 2018 International Conference on Robotics and Automation, pp. 2502–2509 (2018)
Buyval, A., Afanasyev, I., Magid, E.: Comparative analysis of ROS-based monocular SLAM methods for indoor navigation. In: 2016 Ninth International Conference on Machine Vision, ICMV, pp. 103411K (2017)
Bokovoy, A., Yakovlev, K.: Enhancing semi-dense monocular vSLAM used for multi-rotor UAV navigation in indoor environment by fusing IMU data. In: The 2018 International Conference on Artificial Life and Robotics, pp. 391–394 (2018)
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: Large-scale direct monocular SLAM. In: European Conference on Computer Vision, pp. 834–849 (2014)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)
Mur-Artal, R., Tardós, J.D.: Orb-slam2: An open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017)
Gao, X., Wang, R., Demmel, N., Cremers, D.: LDSO: Direct sparse odometry with loop closure. In: International Conference on Intelligent Robots and Systems, pp. 2198–2204 (2018)
Zakiev, A., Lavrenov, R., Magid, E., Svinin, M., Matsuno, F.: Partially unknown environment exploration algorithm for a mobile robot. J. Adv. Res. Dyn. Control Syst. 11(8), 1743–1753 (2019)
Alishev, N., Lavrenov, R., Hsia, K. H., Su, K. L., Magid, E.: Network failure detection and autonomous return algorithms for a crawler mobile robot navigation. In: 11th International Conference on Developments in eSystems Engineering, pp. 169–174 (2018)
Krombach, N., Droeschel, D., Behnke, S.: Combining feature-based and direct methods for semi-dense real-time stereo visual odometry. In: International Conference on Intelligent Autonomous Systems, pp. 855–868 (2016)
Ramil, S., Lavrenov, R., Tsoy, T., Svinin, M., Magid, E.: Real-time video server implementation for a mobile robot. In: 2018 11th International Conference on Developments in eSystems Engineering, pp. 180–185 (2018)
Schubert, D., Goll, T., Demmel, N., Usenko, V., Stückler, J., Cremers, D.: The TUM VI benchmark for evaluating visual-inertial odometry. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1680–1687 (2018)
Engel, J., Usenko, V., Cremers, D.: A photometrically calibrated benchmark for monocular visual odometry. ar**v preprint ar**v:1607.02555 (2016)
Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Siegwart, R.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. 35(10), 1157–1163 (2016)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 573–580(2012)
Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 4, 376–380 (1991)
Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation, pp. 15–22 (2014)
Bescos, B., Fácil, J.M., Civera, J., Neira, J.: DynaSLAM: tracking, map**, and inpainting in dynamic scenes. IEEE Robot. Autom. Lett. 3(4), 4076–4083 (2018)
Acknowledgements
The reported study was funded by the RFBR according to the research project No. 19-58-70002 and research grant of Kazan Federal University.
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Mingachev, E., Lavrenov, R., Magid, E., Svinin, M. (2021). Comparative Analysis of Monocular SLAM Algorithms Using TUM and EuRoC Benchmarks. In: Ronzhin, A., Shishlakov, V. (eds) Proceedings of 15th International Conference on Electromechanics and Robotics "Zavalishin's Readings". Smart Innovation, Systems and Technologies, vol 187. Springer, Singapore. https://doi.org/10.1007/978-981-15-5580-0_28
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