Abstract
The assumption of static scene is typical in SLAM algorithms, which limits the use of visual SLAM systems in real-world dynamic environments. Dynamic elimination, detecting or segmenting the static and dynamic region in the image and regarding the features in dynamic parts as outliers, is proved to an effective solution to solve the dynamic SLAM problem. However, traditional dynamic elimination methods processing each frame are very time consuming. In this paper, dynamic elimination is implemented only on keyframes utilizing YOLO as the fast dynamic detection network. This keyframe-based improvement ensures localization accuracy by ensuring map accuracy, and at the same time increases the speed of the SLAM system with dynamic elimination greatly. Experiments are conducted both in real-world environment and on the public TUM datasets. The results demonstrate the effectiveness as well as efficiency of our method.
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References
Sirmaçek, B., Botteghi, N., Khaled, M.: Reinforcement learning and slam based approach for mobile robot navigation in unknown environments. In: ISPRS Workshop Indoor 3D 2019 (2019)
Marques, F., Costa, P., Castro, F., Parente, M., et al.: Self-supervised subsea slam for autonomous operations. In: Offshore Technology Conference. Offshore Technology Conference (2019)
Liu, R., Zhang, J., Yin, K., Wu, J., Lin, R., Chen, S.: Instant slam initialization for outdoor omnidirectional augmented reality. In: Proceedings of the 31st International Conference on Computer Animation and Social Agents, pp. 66–70. ACM (2018)
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)
Sun, Y., Liu, M., Meng, M.Q.H.: Motion removal for reliable RGB-D SLAM in dynamic environments. Robot. Auton. Syst. 108, 115–128 (2018)
Fan, Y., Han, H., Tang, Y., Zhi, T.: Dynamic objects elimination in slam based on image fusion. Pattern Recogn. Lett. (2018)
Bescos, B., Fácil, J.M., Civera, J., Neira, J.: DynaSLAM: tracking, mapping, and inpainting in dynamic scenes. IEEE Robot. Autom. Lett. 3(4), 4076–4083 (2018)
Yu, C., et al.: DS-SLAM: a semantic visual slam towards dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1168–1174. IEEE (2018)
Qi, X., Yang, S., Yan, Y.: Deep learning based semantic labelling of 3D point cloud in visual slam. In: IOP Conference Series: Materials Science and Engineering, vol. 428, p. 012023. IOP Publishing (2018)
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)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
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Jin, G., Zhong, X., Fang, S., Deng, X., Li, J. (2019). Keyframe-Based Dynamic Elimination SLAM System Using YOLO Detection. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_60
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DOI: https://doi.org/10.1007/978-3-030-27538-9_60
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