visualization of simultaneous localization and mapping using svg - icisbc 2013
TRANSCRIPT
Visualization of Simultaneous
Localization and Mapping using SVG
Harindra W Pradhana, Suryono, Achmad Widodo
Content
Introduction
SLAM
Backgrounds & Objectives
Data Preparation
Movement and Detection Recap
Movement Recap
Detection Recap
Map Generation
Data Composition
Map Plotting
Conclusion
Simultaneous
Localization and
Mapping (SLAM)
SLAM Problems : Posibilities
of generating map and
simultaneously determining
locations of mobile robot
dropped at unknown
location in unknown
environment (Durrant-
Whyte and Bailey, 2006)
• First announced on IEEE
Robotics and Automation
Conference in 1986
Backgrounds & Objectives
Backgrounds Objectives
Why SLAM?
SLAM problem already
solved but still many area of
development
Most research focusing on
efficiency and precission
Contain complex data
carry various information
Why SVG?
Simplicity & compatibility
Building information
system that are
Provide easily
understandable visualization
Adaptable to SLAM system
Using non destructive
observation
Capable to check data
consistency
Data Preparations
Extraction
System log, db queries, etc
Read only, no changed commited to system
Unit conversion
Metric system
Polar coordinate system
Cleaning
System message, redundant record, etc
Consistency check
Continous data stream generate single map, otherwise
generate new map
Movement Recap
• Continuous Discrete
• Each step represented as
vector
• Each movement vector
start at the end of previous
vector
l n=√(∑x=1
x=n
l xsin θ x)2
+(∑x=1
x=n
l xcosθ x)2
θn=tan−1
(∑x=1
x=n
l x sin θx )
(∑x=1
x=n
l x cosθ x)
βn=∑x=1
x=n
θ x
Detection Recap
• Relatively observed by
robot
• Estimate object location
on the map based on
robot estimated position
and orientation
lm=√( l ncosαn+lmn cos( βn+αmn))2+( l nsinαn+lmnsin ( βn+αmn))
2
αm=tan−1 [ ln sinαn+lmn sin(βn+αmn)]
[lncosαn+lmn cos( βn+αmn)]
Map Generation
Data Composition Simulation Data
Map matrix X contain agent Aand objects O X = [ A O ]
Agent matrix A contain historical position An
A = [ A1 A2 A3 … An ]
An = [ ln αn βn ]
Object matrix O contain every object Om on the map O = [ O1 O2 O3 … Om ]
Object Om contain estimated location and detection history Om = [ lm αm Dm ]
Plotting Simulation 1
Detection Map Spring Map
Plotting Simulation 2
Detection Map Spring Map
Plotting Simulation 3
Detection Map Spring Map
Plotting Simulation 4
Detection Map Spring Map
Conclusions & Suggestions
Conclusions Sugestions
SLAM data can be
visualized using XML tags
in SVG form
Data composition
required to standardize
data for visualization
Recapitulation can use
vector addition principles
3D map
Map feature
Zoom
Rotate
Multiple robot
Submap consolidation
Particle Filtering
Thank You