Control Systems and Computers

https://doi.org/10.15407/csc.2024.02.003

Control Systems and Computers, 2024, Issue 2 (306), pp. 3-9

UDC 004.932

A.O. SMIRNOV, PhD Student, Senior Researcher, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine. ORCID: https://orcid.org/0009-0002-6509-4135,
40, Akademika Glushkova Avenue, Kyiv 03187, Ukraine, tonysmn97@gmail.com

DYNAMIC MAP MANAGEMENT FOR GAUSSIAN SPLATTING SLAM

Map representation and management for Simultaneous Localization and Mapping (SLAM) systems is at the core of such algorithms. Being able to efficiently construct new KeyFrames (KF), remove redundant ones, constructing covisibility graphs has direct impact on the performance and accuracy of SLAM. In this work we outline the algorithm for maintaining dynamic map and its management for SLAM algorithm based on Gaussian Splatting as the environment representation. Gaussian Splatting allows for high-fidelity photorealistic environment reconstruction using differentiable rasterization and is able to perform in real-time making it a great candidate for map representation in SLAM. Its end-to-end nature and gradient-based optimization significantly simplifies map optimization, camera pose estimation and KeyFrame management.

 Download full text! (On English)

Keywords: radiance fields, scientific computing, slam, bundle adjustment, gaussian splatting, differentiable rendering.

1. Campos, C., Elvira, R., Rodríguez, J.J.G., Montiel, J.M., & Tardós, J.D. (2021). “Orb-slam3: An accurate open-source library for visual, visual-inertial, and multimap slam”. IEEE Transactions on Robotics, 37 (6), pp. 1874⎯1890. DOI: https://doi.org/10.1109/TRO.2021.3075644
2. Chen, Y., Chen, Y., & Wang, G. (2019). “Bundle adjustment revisited”. arXiv preprint arXiv:1912.03858.
3. Kerbl, B., Kopanas, G., Leimkühler, T., & Drettakis, G. (2023). “3d gaussian splatting for real-time radiance field rendering”. ACM Transactions on Graphics, 42 (4), pp. 1⎯14. arXiv: 2308.04079.
   https://doi.org/10.1145/3592433
4. Kingma, D.P., Ba, J. (2017). “Adam: A Method for Stochastic Optimization”. https://doi.org/10.48550/arXiv.1412.6980.
5. Lim, K.L., & Braunl, T. (2020). “A Review of Visual Odometry Methods and Its Applications for Autonomous Driving”. arXiv 2020. arXiv preprint arXiv:2009.09193.
6. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., & Ng, R. (2021). “Nerf: Representing scenes as neural radiance fields for view synthesis”. Communications of the ACM, 65 (1), pp. 99⎯106. arXiv: 2003.08934.
   https://doi.org/10.1145/3503250
7. Muller, T., Evans, A., Schied, C., & Keller, A. (2022). “Instant neural graphics primitives with a multiresolution hash encoding”. ACM transactions on graphics (TOG), 41 (4), pp. 1⎯15. DOI: 10.1145/3528223.3530127.
   https://doi.org/10.1145/3528223.3530127
8. Mur-Artal, R., Montiel, J. M.M., & Tardos, J.D. (2015). “ORB-SLAM: a versatile and accurate monocular SLAM system”. IEEE transactions on robotics, 31 (5), pp. 1147⎯1163. DOI: 
   https://doi.org/10.1109/TRO.2015.2463671
9. Ren, K., Jiang, L., Lu, T., Yu, M., Xu, L., Ni, Z., & Dai, B. (2024). “Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians”. arXiv preprint arXiv:2403.17898.
10. Shuai, Q., Guo, H., Xu, Zh., Lin, H., Peng, S., Bao, H., Zhou, X. (2024). “Real-Time View Synthesis for Large Scenes with Millions of Square Meters”. [online]. Available at: https://zju3dv.github.io/LoG_webpage/ [Accessed 01 March. 2024].
11. Straub, J., Whelan, T., Ma, L., Chen, Y., Wijmans, E., Green, S., … & Newcombe, R. (2019). “The Replica dataset: A digital replica of indoor spaces”. arXiv preprint arXiv:1906.05797.
12. Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012). “A benchmark for the evaluation of RGB-D SLAM systems”. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pp. 573-580.
    https://doi.org/10.1109/IROS.2012.6385773

Received 15.03.2024