SLAM
¶
This page includes all about SLAM.
Contents:
Introduction of SLAM
SLAM
Probability problem
MAP
EM
Optimization
What is it?
Usage
Pretreatments
Lie group and Lie algebra
Calibration
Camera
IMU (Inertial measurement unit)
Photometric calibration
gamma correction
Exposure time
Vignetting effect
CRF camera response fcn
Calibration
Online calibration
Image Blurry
Cameras
Point Spread Function (PSF)
Laplacian
Singular feature
Alpha Channel
Image Deblur
Reference
Front End : Odometry
Feature Extraction And Match
Direct Methods
SVO (Fast Semi-Direct Monocular Visual Odometry)
DSO(Direct sparse odometry)
Pose Calculation
Match With Map
Multi-sensor Fusion
Our VIO front-end
Feature Extraction
Optical flow
Reference
VINS code
Top Node
call from other
Process
process_loop_detection
process_pose_graph
VINS estimator
preintegration
processIMU
processImage
initialStructure
solveOdometry
slideWindow
optimization
Marginalization
ResidualBlockInfo
MarginalizationInfo
MarginalizationFactor
Feature Manager
Global SfM
Main process loop
triangulate point
solveFrameByPnP
triangulateTwoFrames
construct
VisualIMUAlignment
Gyroscope Bias Calibration
TangentBasis
LinearAlignment
RefineGravity
Factors
Projection Factor
DT optimization
Map Reuse
Keyframe
Loop Detection
Loop Correction
Map save
Summary
Reference
Hessian Matrix
Simulation 1
Simulation 2
Nullspace
Example
First Estimate Jacobian
Gauge Freedom
Analysis methods
Problem elaboration
Prior Gauge
Error and cost result
Covariance processing
Reference
Paper read
Gauge Freedom
比较/分析手段
问题阐述
Prior Gauge的处理
误差和运算cost结果
协方差处理
Marker Localization
Pipeline
PreTreatment
Marker Prepare
Image Preprocess
Feature Extraction
Patch Match
Coefficient
Match Process
With SLAM
Stereo Camera Devices
VINS
Further update thought (22/10/2019)
MY-AR
导航
Contents:
SLAM
Introduction of SLAM
Pretreatments
Front End : Odometry
VINS code
Hessian Matrix
Paper read
Marker Localization
Cost Functions
Server Localization
Other Projects
Paper Read
Related Topics
Documentation overview
Previous:
Welcome to My-AR’s documentation!
Next:
Introduction of SLAM
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