[111]실내이동체정밀위치추정기술의세가지측면 도락주

1. 도 락 주
고려대학교
인지로봇 연구실
[N.E.O]
실내 이동체
정밀 위치 추정기술의
세가지 측면

2. [N.E.O]
Noise : its model and
sources

3. What is Noise?
Noise
Marriam-
Webster
Unwanted electronic signals that harm the quality of
something such as a radio or a digital photograph

4. Noise model : prediction & residual
사람 : 10 사과 : 24
Quotient Residual
2 4
최선을 다하고 남는건 따로 표현

5. Noise model : prediction & residual
Motion Noise
Sensor Noise
Prediction (결정론) Residual (확률론)
위치
변화
현재
위치
제어
입력
측정 예상오차

6. Motion Prediction
Motion Prediction
구간 적분미분 방정식
 정교한 미분 방정식 표현
 Dynamics vs Kinematics
http://android.wond
erhowto.com/
 이산화 과정 (컴퓨터 활용)
 실시간 적분

7. Sensor Prediction
Sensor Prediction
센서 예상 ( )센서 측정 ( )
예상
feature feature ?
robot ?
Physics of sensors
실제 예측 위치

8. Sensor Prediction
Sensor Prediction
센서 예상 ( )센서 측정 ( )
예상 실제 예측 위치
feature feature ?
robot !
Physics of sensors

9. Residual Noise
Possible Selections
Gaussian Noise Uniform NoiseChi-squareMixture-of-Gaussian
Gaussian Noise
Central Limit Theorem Gaussian Distribution
Independent
Random
Variable
+
Many
Trials
확률
변수
평균 분산

10. Coupled Effect of Motion & Sensor
+ =
+
Sensor NoiseMotion Noise

11. Noise
sensor
noise
motion noise
Problem formulation: SLAM

12. Poor Estimation
motion
noise
sensor
noise
real
estimation
Problem formulation: SLAM

13. localization
mapping
SLAM
Simultaneous Localization And Mapping
Problem formulation: SLAM
[ Robotics ] SLAM
[ Computer Vision ] SfM
 장점 : 실시간, 광역공간
 단점 : 영상 정보 부재
 장점 : 실감 영상
 단점 : 지엽적 공간

14. [N.E.O]
Estimation: feature,
data-association and
optimization

15. input
robot
Motion (prediction)
 Position
 Covariance:
Estimation in five minutes

16. input
robot
sensor
feature
Sensing (correction)
 Measurement
 Covariance
Estimation in five minutes

17. Data Association
sensor
feature
© C.Stachiniss
Estimation in five minutes
input
robot

18. sensor
feature
© C.Stachiniss
Estimation in five minutes
Data Association
input
robot

19. sensor
feature
Optimization E K F (Extended Kalman Filter)
 (theoretically) Optimal
 (practically) Much probs.
 Algorithm complexity: O( N2 )
Estimation in five minutes
Linearization
error
Non-Gaussian
noise
input
robot

20. sensor
feature
Estimation in five minutes
Fast SLAM
 Let probability handle every flews
 Assume perfect robot poses : decoupling
 Algorithm complexity : O( N log N )
Linearization
error
Non-Gaussian
noise
input
robot
Optimization

21. sensor
feature
Estimation in five minutes
Graph SLAM
 Spring Network Model
 Heavy Complexity
 Recent advances in matrix sparsity
Linearization
error
Non-Gaussian
noise
input
robot
Optimization

22. [N.E.O]

23. Historical Milestones
2005
New Sensor New Theory New Product
2010
2012
2013
+ =
~80m
Low err
적자
생존
www.wired.com
http://android.wond
erhowto.com/

24. Third Eye Everywhere Virtual Reality
TeeVR

25. user
rotation
user
translation
realistic
sensation
(C) TeeVR
(A) 360 VR
Ⓒ
T
E
E
V
R
(B) 3D SLAM
Ⓐ
3
6
0
V
R
Ⓑ
3D
S
L
A
M
http://www.cinematogra
phydb.com
http://
Matterport.com
Key-feature of TeeVR
http://www.androidpit.fr
http://www.nzvr360.com

26. Sensor Data
Local RGB Global Depth
User ServiceCreation on Demand
User PoseFind
related
data
GPU
rendered
3D map
w1
w2
[ New Approach ] Creation On Demand
Data AcquisitionSensor Data
Local RGB Global Depth
User ServicePrior Creation
[ Previous Approach ] One Prior Creation
Technical Overview

27. S.Y.Yeon, S.H.Ryu, K.Y.Lee, C.H.Jeon, H.A.Choi, J.H.Kang
H.G.Cho, J.H.Hyeon, W.H.Jung, I.S.Baek, T.G.Kim, J.H.Ahn
B.C.Jang, G.H.Lim, J.J.Park, H.Jin, U.Cho
Appreciation goes to our graduate students`

28. Any
Question