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HFS: an intelligent heuristic feature selection scheme to correct uncertainty

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Abstract

In recent years, some researchers have combined deep learning methods such as semantic segmentation with a visual SLAM to improve the performance of classical visual SLAM. However, the above method introduces the uncertainty of the neural network model. To solve the above problems, an improved feature selection method based on information entropy and feature semantic uncertainty is proposed in this paper. The former is used to obtain fewer and higher quality feature points, while the latter is used to correct the uncertainty of the network in feature selection. At the same time, in the initial stage of feature point selection, this paper first filters and eliminates the absolute dynamic object feature points in the a priori information provided by the feature point semantic label. Secondly, the potential static objects can be detected combined with the principle of epipolar geometric constraints. Finally, the semantic uncertainty of features is corrected according to the semantic context. Experiments on the KITTI odometer data set show that compared with SIVO, the translation error is reduced by 12.63% and the rotation error is reduced by 22.09%, indicating that our method has better tracking performance than the baseline method.

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References

  1. Zheng S, Wang J, Rizos C, Ding W, El-Mowafy A (2023) Simultaneous localization and mapping (slam) for autonomous driving: concept and analysis. Remote Sens 15(4):1156

    Article  Google Scholar 

  2. Cai L, Ye Y, Gao X, Li Z, Zhang C (2021) An improved visual slam based on affine transformation for orb feature extraction. Optik 227:165421

    Article  Google Scholar 

  3. Cao Z, Huang Z, Pan L, Zhang S, Liu Z, Fu C (2022) Tctrack: temporal contexts for aerial tracking. arXiv preprint arXiv:2203.01885

  4. Michael E, Summers T, Wood TA, Manzie C, Shames I (2022) Probabilistic data association for semantic slam at scale. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 4359–4364. IEEE

  5. Zhang H (2022) Deep learning applications in simultaneous localization and mapping. In: Journal of Physics: Conference Series, vol. 2181, IOP Publishing, p 012012

  6. Rosen DM, Doherty KJ, Terán Espinoza A, Leonard JJ (2021) Advances in inference and representation for simultaneous localization and mapping. Annu Rev Control, Robot, Auton Syst 4:215–242

    Article  Google Scholar 

  7. Sünderhauf N, Brock O, Scheirer W, Hadsell R, Fox D, Leitner J, Upcroft B, Abbeel P, Burgard W, Milford M, Corke P (2018) The limits and potentials of deep learning for robotics. Int J Robot Res 37(4–5):405–420

    Article  Google Scholar 

  8. Li A, Wang J, Xu M, Chen Z (2021) Dp-slam: a visual slam with moving probability towards dynamic environments. Inf Sci 556:128–142

    Article  Google Scholar 

  9. Ai Y-B, Rui T, Yang X-Q, He J-L, Fu L, Li J-B, Lu M (2021) Visual slam in dynamic environments based on object detection. Def Technol 17(5):1712–1721

    Article  Google Scholar 

  10. Liu N, Shen Z (2021) Sa-lift: a similar area learning invariant feature transform network framework. In: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2021), vol. 11929, SPIE, pp. 38–43

  11. Xu J, Qu K, Yuan M, Yang J (2021) Feature selection combining information theory view and algebraic view in the neighborhood decision system. Entropy 23(6):704

    Article  MathSciNet  Google Scholar 

  12. Qian W, Yu S, Yang J, Wang Y, Zhang J (2020) Multi-label feature selection based on information entropy fusion in multi-source decision system. Evol Intel 13(2):255–268

    Article  Google Scholar 

  13. Nirthika R, Manivannan S, Ramanan A, Wang R (2022) Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study. Neural Comput Appl 34(7):5321–5347

    Article  Google Scholar 

  14. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  15. Jiao J, Zhu Y, Ye H, Huang H, Yun P, Jiang L, Wang L, Liu M (2021) Greedy-based feature selection for efficient lidar slam. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 5222–5228

  16. Laddha P, Omer OJ, Kalsi GS, Mandal DK, Subramoney S (2020) Descriptor scoring for feature selection in real-time visual slam. In: 2020 IEEE International Conference on Image Processing (ICIP), IEEE, pp 2601–2605

  17. Guha R, Khan AH, Singh PK, Sarkar R, Bhattacharjee D (2021) Cga: a new feature selection model for visual human action recognition. Neural Comput Appl 33(10):5267–5286

    Article  Google Scholar 

  18. Gu X, Guo J, Xiao L, Li C (2022) Conditional mutual information-based feature selection algorithm for maximal relevance minimal redundancy. Appl Intell 52(2):1436–1447

    Article  Google Scholar 

  19. Subakan C, Ravanelli M, Cornell S, Bronzi M, Zhong J (2021) Attention is all you need in speech separation. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 21–25

  20. Li D, Miao J, Shi X, Tian Y, Long Q, Cai T, Guo P, Yu H, Yang W, Yue H, et al. (2020) Rap-net: A region-wise and point-wise weighting network to extract robust features for indoor localization. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 1331–1338

  21. Zhu Y, Sun B, Lu X, Jia S (2021) Geographic semantic network for cross-view image geo-localization. IEEE Trans Geosci Remote Sens 60:1–15

    Google Scholar 

  22. Wu H, Wang M, Zhou W, Li H (2021) Learning deep local features with multiple dynamic attentions for large-scale image retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 11416–11425

  23. Xin Z, Cai Y, Lu T, Xing X, Cai S, Zhang J, Yang Y, Wang Y (2019) Localizing discriminative visual landmarks for place recognition. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp 5979–5985

  24. Chen H-Y, Liang J-H, Chang S-C, Pan J-Y, Chen Y-T, Wei W, Juan D-C (2019) Improving adversarial robustness via guided complement entropy. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4881–4889

  25. Bescos B, Fácil JM, Civera J, Neira J (2018) Dynaslam: tracking, mapping, and inpainting in dynamic scenes. IEEE Robot Autom Lett 3(4):4076–4083

    Article  Google Scholar 

  26. Yang S, Fan G, Bai L, Zhao C, Li D (2020) Sgc-vslam: a semantic and geometric constraints vslam for dynamic indoor environments. Sensors 20(8):2432

    Article  Google Scholar 

  27. Dissanayake G, Durrant-Whyte H, Bailey T (2000) A computationally efficient solution to the simultaneous localisation and map building (slam) problem. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 2, IEEE, pp 1009–1014

  28. Davison AJ (2005) Active search for real-time vision. In: Tenth IEEE International Conference on Computer Vision (ICCV’05), vol. 1, IEEE, pp 66–73

  29. Kaess M, Dellaert F (2009) Covariance recovery from a square root information matrix for data association. Robot Auton Syst 57(12):1198–1210

    Article  Google Scholar 

  30. Das A, Waslander SL (2015) Entropy based keyframe selection for multi-camera visual slam. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp 3676–3681

  31. Ryu H (2019) A revisiting method using a covariance traveling salesman problem algorithm for landmark-based simultaneous localization and mapping. Sensors 19(22):4910

    Article  Google Scholar 

  32. Smith L, Gal Y (2018) Understanding measures of uncertainty for adversarial example detection. arXiv preprint arXiv:1803.08533

  33. Liu H, Ji R, Li J, Zhang B, Gao Y, Wu Y, Huang F (2019) Universal adversarial perturbation via prior driven uncertainty approximation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 2941–2949

  34. Pinto L, Davidson J, Sukthankar R, Gupta A (2017) Robust adversarial reinforcement learning. In: International Conference on Machine Learning, PMLR, pp 2817–2826

  35. Ganti P, Waslander SL (2019) Network uncertainty informed semantic feature selection for visual slam. In: 2019 16th Conference on Computer and Robot Vision (CRV), IEEE, pp 121–128

  36. Cho S, Kim C, Park J, Sunwoo M, Jo K (2020) Semantic point cloud mapping of lidar based on probabilistic uncertainty modeling for autonomous driving. Sensors 20(20):5900

    Article  Google Scholar 

  37. Bansal M, Kumar M, Kumar M (2021) 2d object recognition: a comparative analysis of sift, surf and orb feature descriptors. Multimed Tools Appl 80:18839–18857

    Article  Google Scholar 

  38. Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, PMLR, pp 1050–1059

  39. Dechesne C, Lassalle P, Lefèvre S (2021) Bayesian u-net: estimating uncertainty in semantic segmentation of earth observation images. Remote Sens 13(19):3836

    Article  Google Scholar 

  40. Jabir B, Falih N (2021) Dropout, a basic and effective regularization method for a deep learning model: a case study. Indones J Electr Eng Comput Sci 24(2):1009–1016

    Google Scholar 

  41. Anggraeni P, Ramdhan NJ, Asshydiqi MTA, et al. (2021) Implementation of orb-slam-2 algorithm for localization and mapping using monocular camera sensor. In: 2021 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE), IEEE, pp 1–6

  42. Ortega-Gomez JI, Morales-Hernandez LA, Cruz-Albarran IA (2023) A specialized database for autonomous vehicles based on the KITTI vision benchmark. Electronics 12(14):3165

    Article  Google Scholar 

  43. Thitisiriwech K, Panboonyuen T, Kantavat P, Iwahori Y, Kijsirikul B (2022) The bangkok urbanscapes dataset for semantic urban scene understanding using enhanced encoder-decoder with atrous depthwise separable a1 convolutional neural networks. IEEE Access 10:59327–59349

    Article  Google Scholar 

  44. Grupp M (2017) evo: Python package for the evaluation of odometry and slam

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61963017; in part by Shanghai Educational Science Research Project, China, under Grant C2022056; in part by Shanghai Science and Technology Program, China, under Grant 23010501000; in part by Humanities and Social Sciences of Ministry of Education Planning Fund, China, under Grant 22YJAZHA145.

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Authors and Affiliations

  1. School of Electronic Information, China Shanghai Dianji University, No.300 Shuihua Road, Shanghai, 201306, China

    Liu Yanli & Zhang Heng

  2. School of Electrical Engineering, China Shanghai Dianji University, No.300 Shuihua Road, Shanghai, 201306, China

    Xun PengFei

  3. Department of Computer Science and Mathematics, Sul Ross State University, US-90, Alpine, TX, 79830, USA

    Xiong Naixue

  4. School of Computer Science and Engineering, Hunan University of Science, Taoyuan Road, Xiangtan, 411201, China

    Xiong Naixue

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  1. Liu Yanli
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Correspondence to Zhang Heng.

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Yanli, L., PengFei, X., Heng, Z. et al. HFS: an intelligent heuristic feature selection scheme to correct uncertainty. J Supercomput 80, 26250–26279 (2024). https://doi.org/10.1007/s11227-024-06437-7

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