Robust Aged Feature › attachments › seminar...Solve PnP (3D-2D) Essential Mat (2D-2D) Feature...
Transcript of Robust Aged Feature › attachments › seminar...Solve PnP (3D-2D) Essential Mat (2D-2D) Feature...
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Robust Aged Feature
Jeon Hyun Ho
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Contents
Introduction
SLAM
&
Visual Odometry
RAF Future Work
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Introduction
Motivation
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Introduction
Concept
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Introduction
Concept
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Introduction
Concept
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Introduction
Concept
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Introduction
Concept
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SLAM
What is SLAM?
(Simultaneous Localization And Mapping)
A robot is exploring an unknown environment.
- Given
Robot motions
Observations of nearby features
- Estimate
Map
Pose (Position + Orientation)
(Path Planning)
Graph SLAM
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Robot Feature
True
Estimate
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SLAM
Why is SLAM a hard problem?
Robot path and map are both unknown
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SLAM
Why is SLAM a hard problem?
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SLAM
Applications
- Indoor
- Space
- Self-driving car
- Underground
- Undersea
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Visual Odometry
What is Visual Odometry?
A robot is exploring an unknown environment.
- Given
Robot motions
Observations of nearby features (Image, LIDAR)
- Estimate
Map
Pose (Position + Orientation) → σ ∶ Odometry Path Planning
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Visual Odometry
Estimate Pose
Pose
Solve PnP
(3D-2D)Essential Mat
(2D-2D)
Feature Set Detection
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Visual Odometry
Estimate Better Pose
Pose
Solve PnP
(3D-2D)Essential Mat
(2D-2D)
Feature Set Detection
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Pose
Solve PnP
(3D-2D)Essential Mat
(2D-2D)
Feature Set Detection
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Visual Odometry
Estimate Better Pose
Robust Aged Feature
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RAF
RAF Framework Change
- Detection
Color image
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RAF
RAF Framework Change
- Detection
Gray scale image
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RAF
RAF Framework Change
- Detection
Gradient image
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RAF
RAF Framework Change
- Detection
Detection : FAST (Gray)
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RAF
RAF Framework Change
- Detection
Detection : FAST (Gradient)
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RAF
RAF Framework Change
- Detection
Detection : AKAZE - 1
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RAF
RAF Framework Change
- Detection
Detection : AKAZE - 2
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RAF
RAF Framework Change
- Interpolation
Interpolation : X
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RAF
RAF Framework Change
- Interpolation
Interpolation : N x N mask
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RAF
RAF Framework Change
- Interpolation
Interpolation : Bilinear
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RAF
RAF Framework Change
- Filtering
Outlier Filtering
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RAF
RAF Framework Change
- Filtering
Outlier Filtering
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RAF
RAF Framework Change
- Filtering
Stop motion Filtering
avrgDistance
?
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RAF
RAF Framework Change
- Aging
First Detection
- Initial Age
Delete
- Age < Threshold
Aging
- Optical Flow
- Match
De-Aging
- Fail (OF + Match)
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RAF
RAF Framework Change
- Aging
First Detection
- Initial Age
Delete
- Age < Threshold
Aging, (3D)
- Detection (Aging)
- Optical Flow, (Double)
- Match, (Double)
De-Aging
- Fail (OF + Match)
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RAF
RAF Framework Change
- Localization & Mapping
Frist (0, 0, 0) ~ Current
• Auto Loop closing
• Error + Error + Error + …
Previous ~ Current
• Small error
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Implementation
result
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Implementation
result
True
Estimate
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Implementation
result
True
Estimate
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Implementation
result
True
Estimate
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RAF
Future Work
- Detection
Adaptive Feature Detection
- Interpolation
Plane Interpolation
- Filtering
If the mean value of the current frame’s OF >> mean value of the previous N frame’s OF,
Do not work -> ex) car
If the size and mean age are reduced suddenly, Do not work
If the high Age RAF frame number
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RAF
Future Work
- Aging
Outlier De-Aging
- Pose Optimize
Graph SLAM
Loop Closing
Make Weight Function
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Q&A
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SLAM : Self-driving
Overview
- Localization
- Mapping
- Planning
- Control
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SLAM : Self-driving
Localization
- Given
Map
Sensor data
Robot kinematics
- Goal
Find robot position
- ex) Histogram Filter, Kalman Filter, Particle FilterParticle Filter Localization
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SLAM : Self-driving
Tracking
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SLAM : Self-driving
Planning
- Given
Map
Starting position
Goal position
Cost
- Goal
Find minimum cost path
- ex) A*(Path), Dynamic Programing(Policy) Path Planning
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SLAM : Self-driving
Control
- Path is known
- Determine the actual motion commands
- ex) PID
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SLAM
What is SLAM?
A robot is exploring an unknown environment.
- Given
Robot motions
Observations of nearby features
- Estimate
Map
Pose (Position + Orientation)
(Path Planning)
Graph SLAM