Robotic system often use simultaneous localization and mapping method in their operations. Most of the calculation stored as a nested array with multiple level and dimension. SLAM data contains robot movement, object detection and relation between them. This system visualize SLAM data into a map containing robot historical position, object position and relation between object and robot that show detections line from each robot position. The visualized so human eye can understand it. This paper describes the process of movement and detection data composition and conversion to prepare the information required to build a map. The map composed by plotting every movements and detections into polar coordinate area. The map stored into a database for flexible future usage. Commonly used web based interface chosen to display the map via web browser. The map generated by server side scripts that transform polar data into full map.

1. Visualization of Simultaneous
Localization and Mapping using SVG
Harindra W Pradhana, Suryono, Achmad Widodo
wisnu@msi.undip.ac.id, suryono@undip.ac.id, awid@undip.ac.id

2. Content
 Introduction
 SLAM
 Backgrounds & Objectives
 Data Preparation
 Movement and Detection Recap
 Movement Recap
 Detection Recap
 Map Generation
 Data Composition
 Map Plotting
 Conclusion

3. 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

4. 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

5. 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

6. Movement Recap
• Continuous  Discrete
• Each step represented as
vector
• Each movement vector
start at the end of previous
vector
ln=
√(∑
x=1
x=n
l x sin θx)
2
+(∑
x=1
x=n
l x cosθx)
2
θn=tan−1
(∑
x=1
x=n
lx sin θx )
(∑
x=1
x=n
lx cosθx)
βn=∑
x=1
x=n
θx

7. Detection Recap
• Relatively observed by
robot
• Estimate object location
on the map based on
robot estimated position
and orientation
lm=√(lncos αn+lmn cos( βn+αmn))2
+(lnsin αn+lmnsin( βn+αmn))2
αm=tan−1 [ln sin αn+lmn sin( βn+αmn)]
[lncos αn+lmn cos( βn+αmn)]

8. Map Generation
Data Composition Simulation Data
 Map matrix X contain agent A
and 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 ]

9. Plotting Simulation 1
Detection Map Spring Map

10. Plotting Simulation 2
Detection Map Spring Map

11. Plotting Simulation 3
Detection Map Spring Map

12. Plotting Simulation 4
Detection Map Spring Map

13. 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

14. Thank You