VI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (2024)

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  • DaoQing Liao https://ror.org/0530pts50School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    https://ror.org/0530pts50School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

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  • Wei Ai https://ror.org/0530pts50School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    https://ror.org/0530pts50School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

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Journal of Real-Time Image ProcessingVolume 21Issue 2Apr 2024https://doi.org/10.1007/s11554-023-01412-6

Published:09 February 2024Publication History

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Journal of Real-Time Image Processing

Volume 21, Issue 2

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VI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (1)

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Abstract

Abstract

In numerous robotic and autonomous driving tasks, traditional visual SLAM algorithms estimate the camera’s position in a scene through sparse feature points and express the map by estimating the depth of sparse point clouds. However, practical applications require SLAM to create dense maps in real time, overcoming the sparsity and occlusion issues of point clouds. Furthermore, it is advantageous for SLAM map to possess an auto-completion capability, where the map can automatically infer and complete the remaining 20% when the camera observes only 80% of an object. Therefore, a more dense and intelligent map representation is needed. In this paper, we propose a Visual–Inertial SLAM with Neural Radiance Fields reconstruction to address the aforementioned challenges. We integrate the traditional rule-based optimization with NeRF. This approach allows for the real-time update of NeRF local functions by rapidly estimating camera motion and sparse feature point depths to reconstruct 3D scenes. To achieve better camera poses and globally consistent map, we address the issue of IMU noise spikes resulting from rapid motion changes, along with handling pose adjustments due to loop closure fusion. Specifically, we employ a form of widening the static noise covariance to refit the dynamic noise covariance. During loop closure fusion, we treat the pose adjustment between pre- and post-loop closure as a spatiotemporal transformation, migrating NeRF parameters from pre- to post- to expedite loop closure adjustments in NeRF mapping. Moreover, we extend this method to scenarios with only grayscale images. By expanding the color channels of grayscale images and conducting linear spatial mapping, we can rapidly reconstruct 3D scenes with only grayscale images. We demonstrate the precision and speed advantages of our method in both RGB and grayscale scenes.

References

  1. 1. Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. ICCV (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (2)
  2. 2. Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-NeRF 360: unbounded anti-aliased neural radiance fields. CVPR (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (3)
  3. 3. Bhalgat, Y., Laina, I., Henriques, J.F., Zisserman, A., Vedaldi, A.: Contrastive lift: 3D object instance segmentation by slow-fast contrastive fusion. Preprint arXiv:2306.04633 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (4)
  4. 4. Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M.W., Siegwart, R.: The Euroc micro aerial vehicle datasets. Int. J. Robot. Res. (2016). DOI: https://doi.org/10.1177/0278364915620033. https://ijr.sagepub.com/content/early/2016/01/21/0278364915620033.abstractGoogle ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (5)Digital Library
  5. 5. Campos CElvira RRodríguez JJGMontiel JMTardós JDOrb-slam3: an accurate open-source library for visual, visual-inertial, and multimap slamIEEE Trans. Rob.20213761874189010.1109/TRO.2021.3075644Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (7)Cross Ref
  6. 6. Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., Su, H.: Mvsnerf: fast generalizable radiance field reconstruction from multi-view stereo, pp. 14124–14133 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (9)
  7. 7. Chen, Z.: Im-net: learning implicit fields for generative shape modeling (2019)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (10)
  8. 8. Chung, C.M., Tseng, Y.C., Hsu, Y.C., Shi, X.Q., Hua, Y.H., Yeh, J.F., Chen, W.C., Chen, Y.T., Hsu, W.H.: Orbeez-slam: a real-time monocular visual slam with orb features and nerf-realized mapping. Preprint arXiv:2209.13274 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (11)
  9. 9. Clark, R.: Volumetric bundle adjustment for online photorealistic scene capture, pp. 6124–6132 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (12)
  10. 10. Crassidis JLSigma-point Kalman filtering for integrated GPS and inertial navigationIEEE Trans. Aerosp. Electron. Syst.200642275075610.1109/TAES.2006.1642588Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (13)Cross Ref
  11. 11. Dai ANießner MZollhöfer MIzadi STheobalt CBundlefusion: real-time globally consistent 3d reconstruction using on-the-fly surface reintegrationACM Trans Graph (ToG)2017364110.1145/3072959.3054739Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (15)Digital Library
  12. 12. DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: self-supervised interest point detection and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 224–236 (2018)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (17)
  13. 13. Forster CCarlone LDellaert FScaramuzza DOn-manifold preintegration for real-time visual-inertial odometryIEEE Trans. Rob.201633112110.1109/TRO.2016.2597321Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (18)Digital Library
  14. 14. Godard, C., MacAodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency, pp. 270–279 (2017)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (20)
  15. 15. Godard, C., MacAodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation, pp. 3828–3838 (2019)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (21)
  16. 16. Koestler, L., Yang, N., Zeller, N., Cremers, D.: Tandem: tracking and dense mapping in real-time using deep multi-view stereo, pp. 34–45 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (22)
  17. 17. Leutenegger SFurgale PRabaud VChli MKonolige KSiegwart RKeyframe-based visual-inertial slam using nonlinear optimizationProc. Robot. Sci. Syst. (RSS)201320131Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (23)
  18. 18. Leutenegger SLynen SBosse MSiegwart RFurgale PKeyframe-based visual–inertial odometry using nonlinear optimizationInt. J. Robot. Res.201534331433410.1177/0278364914554813Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (24)Digital Library
  19. 19. Li, J., Feng, Z., She, Q., Ding, H., Wang, C., Lee, G.H.: Mine: Towards continuous depth MPI with nerf for novel view synthesis, pp. 12578–12588 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (26)
  20. 20. Li MMourikis AIHigh-precision, consistent EKF-based visual-inertial odometryInt. J. Robot. Res.201332669071110.1177/0278364913481251Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (27)Digital Library
  21. 21. Li, Z., Wang, Q., Cole, F., Tucker, R., Snavely, N.: Dynibar: neural dynamic image-based rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4273–4284 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (29)
  22. 22. Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: bundle-adjusting neural radiance fields (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (30)
  23. 23. Lindenberger, P., Sarlin, P.E., Pollefeys, M.: Lightglue: local feature matching at light speed. Preprint arXiv:2306.13643 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (31)
  24. 24. Lupton, T., Sukkarieh, S.: Visual–inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Trans. Robot. (2011). DOI: https://doi.org/10.1109/tro.2011.2170332. http://dx.doi.org/10.1109/tro.2011.2170332Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (32)Digital Library
  25. 25. Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., Duckworth, D.: NeRF in the wild: neural radiance fields for unconstrained photo collections (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (34)
  26. 26. Meng, X., Chen, W., Yang, B.: Neat: learning neural implicit surfaces with arbitrary topologies from multi-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 248–258 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (35)
  27. 27. Mildenhall BSrinivasan PPTancik MBarron JTRamamoorthi RNg RNeRF: representing scenes as neural radiance fields for view synthesisCommun. ACM20216519910610.1145/3503250Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (36)Digital Library
  28. 28. Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint Kalman filter for vision-aided inertial navigation, pp. 3565–3572 (2007)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (38)
  29. 29. Müller TEvans ASchied CKeller AInstant neural graphics primitives with a multiresolution hash encodingACM Trans. Graph. (ToG)202241411510.1145/3528223.3530127Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (39)Digital Library
  30. 30. Ortiz, J., Clegg, A., Dong, J., Sucar, E., Novotny, D., Zollhoefer, M., Mukadam, M.: isdf: Real-time neural signed distance fields for robot perception. Preprint arXiv:2204.02296 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (41)
  31. 31. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation, pp. 165–174 (2019)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (42)
  32. 32. Paul, M.K., Roumeliotis, S.I.: Alternating-stereo vins: observability analysis and performance evaluation, pp. 4729–4737 (2018)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (43)
  33. 33. Paul, M.K., Wu, K., Hesch, J.A., Nerurkar, E.D., Roumeliotis, S.I.: A comparative analysis of tightly-coupled monocular, binocular, and stereo vins, pp. 165–172 (2017)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (44)
  34. 34. Prisacariu, V.A., Kähler, O., Golodetz, S., Sapienza, M., Cavallari, T., Torr, P.H., Murray, D.W.: Infinitam v3: A framework for large-scale 3d reconstruction with loop closure. arXiv preprint arXiv:1708.00783 (2017)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (45)
  35. 35. Qin TLi PShen SVins-mono: a robust and versatile monocular visual-inertial state estimatorIEEE Trans. Rob.20183441004102010.1109/TRO.2018.2853729Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (46)Digital Library
  36. 36. Qin, T., Pan, J., Cao, S., Shen, S.: A general optimization-based framework for local odometry estimation with multiple sensors. Preprint arXiv:1901.03638 (2019)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (48)
  37. 37. Rosinol, A., Leonard, J.J., Carlone, L.: NeRF-SLAM: real-time dense monocular slam with neural radiance fields. Preprint arXiv:2210.13641 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (49)
  38. 38. Straub, J., Whelan, T., Ma, L., Chen, Y., Wijmans, E., Green, S., Engel, J.J., Mur-Artal, R., Ren, C., Verma, S., etal.: The replica dataset: a digital replica of indoor spaces. Preprint arXiv:1906.05797 (2019)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (50)
  39. 39. Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: imap: implicit mapping and positioning in real-time, pp. 6229–6238 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (51)
  40. 40. Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. CVPR (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (52)
  41. 41. Teed ZDeng JDroid-slam: deep visual slam for monocular, stereo, and RGB-D camerasAdv. Neural. Inf. Process. Syst.2021341655816569Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (53)
  42. 42. Tretschk, E., Tewari, A., Golyanik, V., Zollhöfer, M., Lassner, C., Theobalt, C.: Non-rigid neural radiance fields: reconstruction and novel view synthesis of a dynamic scene from monocular video. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12959–12970 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (54)
  43. 43. Wang, P., Liu, Y., Chen, Z., Liu, L., Liu, Z., Komura, T., Theobalt, C., Wang, W.: F2-nerf: fast neural radiance field training with free camera trajectories. Preprint arXiv:2303.15951 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (55)
  44. 44. Wang, Y., Han, Q., Habermann, M., Daniilidis, K., Theobalt, C., Liu, L.: Neus2: fast learning of neural implicit surfaces for multi-view reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3295–3306 (2023)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (56)
  45. 45. Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: Elasticfusion: Dense slam without a pose graph (2015)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (57)
  46. 46. Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: inerf: inverting neural radiance fields for pose estimation. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1323–1330. IEEE (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (58)
  47. 47. Yu, A., Fridovich-Keil, S., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. Preprint arXiv:2112.05131 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (59)
  48. 48. Zhi, S., Laidlow, T., Leutenegger, S., Davison, A.J.: In-place scene labelling and understanding with implicit scene representation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15838–15847 (2021)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (60)
  49. 49. Zhu, Z., Peng, S., Larsson, V., Xu, W., Bao, H., Cui, Z., Oswald, M.R., Pollefeys, M.: Nice-slam: neural implicit scalable encoding for slam, pp. 12786–12796 (2022)Google ScholarVI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (61)

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      VI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping (63)

      Journal of Real-Time Image Processing Volume 21, Issue 2

      Apr 2024

      529 pages

      ISSN:1861-8200

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      © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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          Publication History

          • Published: 9 February 2024
          • Accepted: 30 December 2023
          • Received: 5 December 2023

          Author Tags

          • NeRF
          • SLAM
          • Intelligent map
          • Real-time online algorithm

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