論文閱讀LR LIO-SAM

時間 2020-12-28

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Abstract

緊耦合lidar inertial里程計, 用smoothing和mapping.app

1. Introduction

緊耦合lidar-inertial里程計.框架

緊耦合的lidar inertial里程計框架

通常都是用ICP或者是GICP.dom

在LOAM[1], IMU被引入來de-skew lidar scan, 而後給移動一個先驗作scan-匹配.oop

在[15], 預積分IMU測量被用來 de-skew 點雲.spa

一個robocentric lidar-inertial 狀態估計器, R-LINS[16] , 用error-state KF.orm

LIOM只能 0.6 倍實時blog

3. LiDAR Inertial Odometry via SAM

A. System Overview

狀態是:io

\[\mathbf{x}=\left[\mathbf{R}^{\mathbf{T}}, \mathbf{p}^{\mathbf{T}}, \mathbf{v}^{\mathbf{T}}, \mathbf{b}^{\mathbf{T}}\right]^{\mathbf{T}} \]

B. IMU Preintegration Factor

角速度, 加速度的測量:form

\[\begin{array}{l} \hat{\boldsymbol{\omega}}_{t}=\boldsymbol{\omega}_{t}+\mathbf{b}_{t}^{\boldsymbol{\omega}}+\mathbf{n}_{t}^{\boldsymbol{\omega}} \\ \hat{\mathbf{a}}_{t}=\mathbf{R}_{t}^{\mathbf{B W}}\left(\mathbf{a}_{t}-\mathbf{g}\right)+\mathbf{b}_{t}^{\mathbf{a}}+\mathbf{n}_{t}^{\mathbf{a}}, \end{array} \]

這裏 \(\hat{\omega}_t\) 和 \(\hat{a}_t\) 是 raw 測量在 \(B\) 系.class

速度, 位置和旋轉在 \(t+\Delta t\)時刻以下:

\[\begin{aligned} \mathbf{v}_{t+\Delta t}=\mathbf{v}_{t}+\mathbf{g} \Delta t+\mathbf{R}_{t}\left(\hat{\mathbf{a}}_{t}-\mathbf{b}_{t}^{\mathbf{a}}-\mathbf{n}_{t}^{\mathbf{a}}\right) \Delta t \\ \mathbf{p}_{t+\Delta t}=\mathbf{p}_{t}+\mathbf{v}_{t} \Delta t+\frac{1}{2} \mathbf{g} \Delta t^{2} \\ &+\frac{1}{2} \mathbf{R}_{t}\left(\hat{\mathbf{a}}_{t}-\mathbf{b}_{t}^{\mathbf{a}}-\mathbf{n}_{t}^{\mathbf{a}}\right) \Delta t^{2} \\ \mathbf{R}_{t+\Delta t}=\mathbf{R}_{t} \exp \left(\left(\hat{\boldsymbol{\omega}}_{t}-\mathbf{b}_{t}^{\omega}-\mathbf{n}_{t}^{\omega}\right) \Delta t\right) \end{aligned} \]

這裏 \(R_t = R_t^{WB} = R_t^{{BW}^T}\). 這裏咱們假設角速度和加速度的\(B\) 保持不變.

C. LiDAR Odometry Factor

當一個新的scan到達時, 咱們先作特徵提取. Edge / planar 特徵被提取來估計局部點的roughness. 有大的 roughness值的實被分類爲edge, 值小的就是planar特徵.

1. Sub-keyframes for voxel map

2. Scan-matching

3. Relative transform

edge點和平面點對應以下:

\[\begin{array}{r} \mathbf{d}_{e_{k}}=\frac{\left|\left(\mathbf{p}_{i+1, k}^{e}-\mathbf{p}_{i, u}^{e}\right) \times\left(\mathbf{p}_{i+1, k}^{e}-\mathbf{p}_{i, v}^{e}\right)\right|}{\left|\mathbf{p}_{i, u}^{e}-\mathbf{p}_{i, v}^{e}\right|} \\ \mathbf{d}_{p_{k}}=\frac{\left(\mathbf{p}_{i, u}^{p}-\mathbf{p}_{i, v}^{p}\right) \times\left(\mathbf{p}_{i, u}^{p}-\mathbf{p}_{i, w}^{p}\right) \mid}{\left|\left(\mathbf{p}_{i, u}^{p}-\mathbf{p}_{i, v}^{p}\right) \times\left(\mathbf{p}_{i, u}^{p}-\mathbf{p}_{i, w}^{p}\right)\right|} \end{array} \]