2019-04-10 1

Robust Aged Feature

Jeon Hyun Ho

2

Contents

Introduction

SLAM

&

Visual Odometry

RAF Future Work

3

Introduction

Motivation

4

Introduction

Concept

View

5

Introduction

Concept

View

6

Introduction

Concept

View

7

Introduction

Concept

View

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Introduction

Concept

View

9

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

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

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