ENTROPY-BASED FILTERING FOR INTERPOLATION-ASSISTED SLAM

Gerald Liu∗ and Chao Shen∗∗

References

  1. [1] A.J. Davison, I.D. Reid, N.D. Molton, and O. Stasse,MonoSLAM: Real-time single camera SLAM, IEEE Transac-tions on Pattern Analysis and Machine Intelligence, 29(6),2007, 1052–1067.
  2. [2] S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M.J. Black, andR. Szeliski, A database and evaluation methodology for opticalflow, International Journal of Computer Vision, 92(1), 2011,1–31.
  3. [3] Z. Huang, T. Zhang, W. Heng, B. Shi, and S. Zhou, Real-timeintermediate flow estimation for video frame interpolation,in Proceedings of European Conference on Computer Vision(ECCV), 2022, 1–22.
  4. [4] C. Campos, R. Elvira, J.J.G. Rodr´ıguez, J.M.M. Montiel,and J.D. Tard´os, ORB-SLAM3: An accurate open-sourcelibrary for visual, visual-inertial and multi-map SLAM, IEEETransactions on Robotics, 37(6), 2021, 1874–1890.
  5. [5] Z. Zhu, J. Wang, M. Xu, S. Lin, and Z. Chen, Interpolation-SLAM: An effective visual SLAM system based on interpolationnetwork, Engineering Applications of Artificial Intelligence,115, 2022, 105333.
  6. [6] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, ORB:An efficient alternative to SIFT or SURF, in Proceedingsof IEEE International Conference Computer Vision (ICCV),2011, 2564–2571.
  7. [7] J. Canny, A computational approach to edge detection, IEEETransactions on Pattern Analysis and Machine Intelligence,PAMI-8(6), 1986, 679–698.10
  8. [8] A. Geiger, P. Lenz, and R. Urtasun, Are we ready forautonomous driving? The KITTI vision benchmark suite, inProceedings of the IEEE Conference on Computer Vision andPattern Recognition (CVPR), 2012, 3354–3361.
  9. [9] M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S.Omari, M.W. Achtelik, and R. Siegwart, The EuRoC microaerial vehicle datasets, The International Journal of RoboticsResearch, 35(10), 2016, 1157–1163.
  10. [10] J. Engel, V. Koltun, and D. Cremers, Direct sparse odometry,2016, arXiv:1607.02565.
  11. [11] J. Engel, T. Schoeps, and D. Cremers, LSD-SLAM: Large-scaledirect monocular SLAM, in Proceedings European Conferenceon Computer Vision (ECCV), 2014, 1–16.
  12. [12] T. Hu, T. Scargill, F. Yang, Y. Chen, G. Lan, and M. Gorlatova,SEESys: Online pose error estimation system for visual SLAM,in Proceedings of the 22nd ACM Conference on EmbeddedNetworked Sensor Systems (SenSys), 2024, 322–335.
  13. [13] G. Chu, Y. Peng, and X. Luo, ALGD-ORB: An improvedimage feature extraction algorithm with adaptive thresholdand local gray difference, PLoS One, 18, 2023, e0293111.
  14. [14] Y. Zhao and P.A. Vela, Good feature matching: Towardaccurate, robust VO/VSLAM with low latency, IEEETransactions on Robotics, 36(3), 2020, 657–675.
  15. [15] A. Tourani, H. Bavle, J.L. Sanchez-Lopez, and H. Voos, VisualSLAM: What are the current trends and what to expect?Sensors, 22(23), 2022, 9297.
  16. [16] D. Kye, C. Roh, S. Ko, C. Eom, and J. Oh, AceVFI: Acomprehensive survey of advances in video frame interpolation,2025, arXiv:2506.01061.
  17. [17] Z. Zhang, L. Chen, R. Xie, and L. Song, Frame interpolationvia refined deep voxel flow, in Proceedings 2018 25th IEEEInternational Conference on Image Processing (ICIP), 2018,1473–1477.
  18. [18] H. Lee, T. Kim, T. Chung, D. Pak, Y. Ban, and S. Lee, AdaCoF:Adaptive collaboration of flows for video frame interpolation,2020, arXiv:1907.10244.
  19. [19] H. Jiang, D. Sun, V. Jampani, M.H. Yang, E. Learned-Miller, and J. Kautz, Super SloMo: High quality estimationof multiple intermediate frames for video interpolation, 2018,arXiv:1712.00080.
  20. [20] X. Gao and T. Zhang, Introduction to visual SLAM: Fromtheory to practice, (Singapore: Springer, 2021).
  21. [21] Z. Zhang, H. Chen, H. Zhao, G. Lu, Y. Fu, H. Xu, and Z.Wu, EDEN: Enhanced diffusion for high-quality large- motionvideo frame interpolation, 2025, arXiv:2503.15831.
  22. [22] P. Han, F. Zhang, B. Zhao, and X. Li, Motion-aware videoframe interpolation, 2024, arXiv:2402.02892.
  23. [23] J. Ni, Y. Chen, K. Wang, and S.X. Yang, An improved vision-based SLAM approach inspired from animal spatial cognition,International Journal of Robotics and Automation, 2019,491–502.
  24. [24] B. Han and L. Xu, MLC-SLAM: Mask loop closing formonocular SLAM, International Journal of Robotics andAutomation, 37, 2022, 107–114.

Important Links:

Go Back