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Young Ki BAIK. Vision-based SLAM Enhanced by Particle Swarm Optimization on the Euclidean Group. Vision seminar : Dec. 30. 2009. Computer Vision Lab. Outline. Introduction. Related works. Problem statement. Proposed algorithm. PSO-based visual SLAM.

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young ki baik
Young Ki BAIK

Vision-based SLAM Enhanced by Particle Swarm Optimizationon the Euclidean Group

Vision seminar : Dec. 30. 2009

Computer Vision Lab.

outline
Outline

Introduction

Related works

Problem statement

Proposed algorithm

PSO-based visual SLAM

Single camera SLAM using ABC algorithm

Demonstration

Conclusion

what is slam
What is SLAM?

SLAM : Simultaneous Localization And Mapping

why visual slam
Why visual SLAM?

To acquire observation data

Use many different type of sensor

Laser rangefinders, Sonar sensors

Too expensive : about 2000$

Scanning system : complex mechanics

Camera

Low price : about 30$

Acquire large and meaningful information

from one shot measure

how to solve slam problem
How to solve SLAM problem?

SLAM problem

Solved by filtering approaches

Extended Kalman Filter (EKF)

has scalability problem of the map

Rao-Blackwellised Particle Filter (RBPF)

handles nonlinear and non-Gaussian

reduces computation cost by decomposing sampling space

previous works
Previous works

EKF-based visual SLAM

Andrew Davison (1998)

Stereo camera + odometry

Andrew Davison (2002)

Single camera without odometry

RBPF-based visual SLAM

Robert Sim (2005)

Stereo camera + odometry

Mark Pupilli (2005)

Single camera without odometry

rbpf slam
RBPF-SLAM

State equation

(Process noise)

(User input or odometry)

(Nonlinear stochastic difference equation)

Measurement equation

(Measurement noise)

(Camera projection function)

problem of rbpf slam
Problem of RBPF-SLAM

How to choose importance function?

?

t+1

t

Odometry

Naive motion model

Constant position

Xt+1=Xt+N

AngleChange

+

DistanceChange

Constant velocity

Xt+1=Xt+∇t(Vt+N)

Left Encoder Distance

RightEncoder Distance

problem of rbpf slam1
Problem of RBPF-SLAM

Sampling by transition model

Landmark

Particle

Robot

t

problem of rbpf slam2
Problem of RBPF-SLAM

Sampling by transition model

t

t+1

problem of rbpf slam3
Problem of RBPF-SLAM

Sampling by transition model

t

t+1

problem of rbpf slam4
Problem of RBPF-SLAM

Sampling by transition model

t+1

(Gaussian)

problem of rbpf slam5
Problem of RBPF-SLAM

Sampling by transition model

t+1

problem of rbpf slam6
Problem of RBPF-SLAM

How to choose importance function?

Hand-held camera case

?

t+1

t

rbpf slam1
RBPF-SLAM

Sampling by transition model

t

t+1

problem of rbpf slam7
Problem of RBPF-SLAM

Particle impoverishment

Mismatch between proposal and likelihood distribution.

Likelihood

Proposal

optimal importance function oif
Optimal Importance Function (OIF)

For better proposal distribution

Use observation for proposal distribution

Optimal importance function approach

(Doucet et al., 2000)

  • Observation incorporated proposal
  • Linearize the optimal importance function
  • Used in FastSLAM 2.0 (Montemerlo et al.)

The state of the art !!

optimal importance function oif1
Optimal Importance Function (OIF)

Sampling by optimal importance function

OIF

t

t+1

problem of oif based slam
Problem of OIF-based SLAM

Linearization Error

Smooth camera motion

Abrupt camera motion

: Real camera state

Linearization Error

: Estimated camera state by linearization

: Predicted camera state by a motion model

problem statement
Problem statement

OIF-based visual SLAM

State of the art

Weak to abrupt camera motion

Novel visual SLAM

robust to abrupt camera motion

target
Target

Proposed SLAM system

6-DOF SLAM

Hand-held camera

Single or stereo camera

No odometry

RBPF-based SLAM

Robust to sudden changes

Real-time system

our contribution
Our contribution

We propose …

Novel particle filtering framework

combined with geometric PSO

Based on special Euclidean group SE (3)

Reformulating original PSO in consideration of SE (3)

Applying Quantum particles

to more actively explore the problem space

Robust to abrupt camera motion!!

special euclidean group se 3
Special Euclidean group SE (3)

Ignores geometry of the underlying space

Considers geometry of the curved space!

special euclidean group se 31
Special Euclidean group SE (3)

6D vector  Euclideangroup SE(3)

Lie group Group + Differentiable manifold

Lie algebra  Tangent space at the identity (se(3))

Origin

se(3)

Exp

Log

Exp: se(3)  SE(3)

Log: SE(3)  se(3)

Identity

SE(3)

special euclidean group se 32
Special Euclidean group SE (3)

6D vector  Euclideangroup SE(3)

Sampling on Tangent space at the identity (se(3))

Reasonable to consider the geometry of motion

Sampling

Exp

se(3)

SE(3)

main idea
Main idea

We use optimization method for better proposal distribution…

Particle Swarm Optimization

Prior

Propagate particles using motion prior

PSO Moves

Particles

with high likelihood

particle swarm optimization
Particle Swarm Optimization

Developed in evolutionary computation community

Sampling-based optimization method

Uses the relationship between particles

PSO

OIF

Linearization

Interaction

particle swarm optimization1
Particle Swarm Optimization

Particle from motion prior

particle swarm optimization2
Particle Swarm Optimization

Initialization

(current optimum)

(individual best)

particle swarm optimization3
Particle Swarm Optimization

Particle from motion prior

(current optimum)

(individual best)

particle swarm optimization4
Particle Swarm Optimization

Particle from motion prior

(current optimum)

(individual best)

(Inertia)

(Coefficient)

(Random)

particle swarm optimization5
Particle Swarm Optimization

Velocity updating

(current optimum)

(individual best)

particle swarm optimization6
Particle Swarm Optimization

Moving

(current optimum)

(individual best)

particle swarm optimization7
Particle Swarm Optimization

Global and local best updating

(current optimum)

(individual best)

geometric particle swarm optimization
Geometric Particle Swarm Optimization

Tangent space at

Manifold

Random perturbation &

coefficient multiplication

experiments
Experiments

System environment

CPU : Intel Core-2 Quad 2.4 GHz process

Real-time with C++ implementation

Synthetic sequence

Real sequence

Virtual stereo camera

Bumblebee stereo camera

(BB-HICOL-60)

Quantitative analysis

artificial bee colony
Artificial Bee Colony

Additional work !!

Visual Odometry

Determining the position and orientation of a robot

by analyzing the associated camera images …

David Nister (2004)

Monocular or binocular camera

Yang Cheng et al. (2008)

Stereo camera

artificial bee colony1
Artificial Bee Colony

Additional work !!

Propagate particles

via visual odometry

Propagate particles using motion prior

PSO Moves

PSO Moves

Particles

with high likelihood

Artificial Bee Colony

conclusion
Conclusion

Novel visual SLAM is presented !!

RBPF based on the special Euclidean group SE (3)

Geometric Particle Swarm Optimization

Robust to abrupt camera motion

Real-time system

Novel monocular SLAM will be presented !!

Geometric Artificial Bee Colony

Combined proposal ( VO + Naive motion model )