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  • 12 min read

Derivation of On-Manifold IMU Preintegration

  • C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “On-Manifold Preintegration for Real-Time Visual--Inertial Odometry,” IEEE Trans. Robot., vol. 33, no. 1, pp. 1–21, Feb. 2017, doi: 10.1109/TRO.2016.2597321.
  • Z. Yang and S. Shen, “Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration,” IEEE Trans. Automat. Sci. Eng., vol. 14, no. 1, pp. 39–51, Jan. 2017, doi: 10.1109/TASE.2016.2550621.

IMU preintegration is a technique in visual-inertial odometry that efficiently fuses high-frequency IMU data between keyframes. Using Lie group theory on \(SE(3)\), it handles nonlinear 3D rotations and precomputes motion constraints for optimization. This method accounts for sensor biases, noise, and is essential for real-time state estimation.

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  • 8 min read

Innovations in BIM-based Localization

  • H. Yin, J. M. Liew, W. L. Lee, M. H. Ang, Ker-Wei Yeoh, and Justin, “Towards BIM-based robot localization: a real-world case study,” presented at the 39th International Symposium on Automation and Robotics in Construction, Jul. 2022. doi: 10.22260/ISARC2022/0012.
  • H. Yin, Z. Lin, and J. K. W. Yeoh, “Semantic localization on BIM-generated maps using a 3D LiDAR sensor,” Automation in Construction, vol. 146, p. 104641, Feb. 2023, doi: 10.1016/j.autcon.2022.104641.
  • Z. Qiao et al., “Speak the Same Language: Global LiDAR Registration on BIM Using Pose Hough Transform,” IEEE Transactions on Automation Science and Engineering, pp. 1–1, 2025, doi: 10.1109/TASE.2025.3549176.

In traditional SLAM, mapping and localization occur simultaneously, with maps built incrementally. In construction, maps are created once to support long-term operations, and BIM is increasingly favored over CAD. The following studies explore BIM-based localization, semantic consistency, and geometric consistency.

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  • 8 min read

Sparsity Extended Information Filter SLAM

M. R. Walter, R. M. Eustice, and J. J. Leonard, “Exactly Sparse Extended Information Filters for Feature-based SLAM,” The International Journal of Robotics Research, vol. 26, no. 4, pp. 335–359, Apr. 2007, doi: 10.1177/0278364906075026.

稀疏扩展信息滤波器（SEIF）SLAM 是一种计算高效的同时定位与地图构建（SLAM）方法。通过利用信息矩阵的稀疏性，SEIF 降低了状态估计的计算复杂度，使其适用于大规模环境。

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  • 6 min read

Robust Initialization of VINS

T. Qin and S. Shen, “Robust initialization of monocular visual-inertial estimation on aerial robots,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC: IEEE, Sep. 2017, pp. 4225–4232. doi: 10.1109/IROS.2017.8206284.

这篇文章提出了一种鲁棒的视觉惯性系统初始化方法，通过松耦合的方式对齐 IMU 与视觉数据，主要解决了外参数标定、陀螺仪偏置估计、速度重力尺度和视觉惯性对齐等关键问题。

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  • 3 min read

A Continuous-time VINS

S. Lovegrove, A. Patron-Perez, and G. Sibley, “Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras,” in Procedings of the British Machine Vision Conference 2013, Bristol: British Machine Vision Association, 2013, p. 93.1-93.11. doi: 10.5244/C.27.93.

这篇论文主要工作是建立了卷帘相机的连续时域下的数学模型，初始化方法是利用 IMU 主动对齐视觉。

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  • 2 min read

Handling Gauge Freedom in VINS

Z. Zhang, G. Gallego, and D. Scaramuzza, "On the Comparison of Gauge Freedom Handling in Optimization-Based Visual-Inertial State Estimation," IEEE Robot. Autom. Lett., vol. 3, no. 3, pp. 2710–2717, Jul. 2018, doi: 10.1109/LRA.2018.2833152.

这篇论文主要讨论了视觉惯性系统中规范自由度（Gauge Freedom）的处理方法，并对比了不同策略的效果。