English 썸네일형 리스트형 [En] Notes on Multiple View Geometry in Computer Vision Part 1 0. Projective Space Projective geometry is an essential topic in computer vision, particularly in 3D reconstruction and Simultaneous Localization and Mapping (SLAM) algorithms. Projective geometry is the study of properties that are invariant under projective transformations. A projective transformation is a non-singular linear transformation of the projective space $\mathbb{P}^{n}$. In projecti.. 더보기 [SLAM][En] Direct Sparse Odometry (DSO) Paper Review Part 2 4. Keyframes 4.1. Inverse Depth Update To be added 4.2. Immature Point Activation To be added 4.3. Sliding Window Optimization 4.3.1. Error Function Formulation If a specific frame is determined as a keyframe, the error function must be updated between keyframes within the sliding window and a new keyframe. At this time, Local Bundle Adjustment (LBA) is performed to optimize not only the keyfram.. 더보기 [SLAM][En] Direct Sparse Odometry (DSO) Paper Review Part 1 In this post, we review the DSO paper, which is famous for its direct method-based VO algorithm. While analyzing the DSO code, I found out that there were a lot of details omitted in the thesis, and I wrote a summary that included formula derivation and code review by referring to other people's already well-organized data. 1. Initialization 1.1 Calibration Since the direct method estimates the .. 더보기 [En] Notes on Lie Theory (SO(3), SE(3)) In this post, Lie Theory used in SLAM is explained. When studying the optimization part in SLAM, Lie Theory-based optimization methods often appear, but it is difficult to understand the optimization process without prior knowledge of the content. did Most of the contents were referred to [6]. Group Theory A group refers to an algebraic structure consisting of a set and a binary operation, which.. 더보기 [En] Notes on Plücker Coordinate 1. Introduction The Plücker Coordinate representation was first introduced by the 19th century mathematician Julius Plücker. This expression is one of the ways to express a line, and is used to express a line in the 4-dimensional $\mathbb{P}^{3}$ space using a point in the 6-dimensional $\mathbb{P}^{5}$ space. do. This expression method is characterized by a one-to-one correspondence between a p.. 더보기 [SLAM][En] Errors and Jacobian Derivations for SLAM Part 2 7. IMU measurement error In order to obtain the IMU measurement error, you must first know about the IMU preintegration technique and error-state modeling. The following figure expresses the overall IMU measurement error-based optimization process. The overall IMU measurement error-based optimization process, with detailed explanations and color highlighting added for better understanding, is as.. 더보기 [SLAM][En] Errors and Jacobian Derivations for SLAM Part 1 1. Introduction This post explains the definition of various errors used in SLAM and the Jacobian derivations for nonlinear optimization. The errors covered in this post are: Reprojection error \begin{equation} \begin{aligned} \mathbf{e} & = \mathbf{p} - \hat{\mathbf{p}} \in \mathbb{R}^{2} \end{aligned} \end{equation} Photometric error \begin{equation} \begin{aligned} \mathbf{e} & = \mathbf{I}_{.. 더보기 [En] Vim - Useful plugins and C++/Python development environment 1. Introduction This post explains how to build C++/Python development environment through Vim editor on Linux. In this post, vim's code navigations plugin used youcompleteme and vim-lsp, and youcompleteme was a uniquely powerful code navigation plugin in vim before the vim-lsp came out in 2016. However, as vim-lsp supporting various programming languages appeared, both are now widely used. As o.. 더보기 [SLAM][En] Notes on Formula Derivation and Analysis of the VINS-mono This post is the study note on Yibin Wu's "Formula Derivation and Analysis of the VINS-mono" paper. If you see something wrong or needs to be corrected, please comment, and I will check it later. 1. Introduction Visualizing the camera and IMU coordinate system is as follows. Both sensors are fixed on a single board, and calibrating the board yields the extrinsic parameter $\mathbf{T}^{b}_{C}$, w.. 더보기 이전 1 다음
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