Marek Ososinski, Aberystwyth University

1. Optimisation of laser data acquisition
Robots and lasers
Marek Ososinski
Aberystwyth University
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2. Layout
1 Introduction
2 Project Desription
3 System overview
4 Environment Mapping
5 Reasoning, Acquisition and Validation
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3. About me
Name: Marek Ososinski (mro7@aber.ac.uk)
Place: Aberystwyth University
Background: BSc in Artificial Intelligence
Funding: KESS
Partners: Royal Commission on Ancient and Historical Monuments of
Wales
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4. A bit of history
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5. Project Description
Project:
The aim of the project is to provide a methodology for laser data
acquisition, that will esnure best quality of data within the given
time-constraints.
PhD:
Creation of a robotic system capable of mapping the area and
detecting best scanning positions, that will ensure completeness and
minimise registration error.
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6. General idea
to here
getting from here
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7. Optimisation of laser data acquisition
Optimisation factors:
Time
Registration error
Scan completeness
Processing effort
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8. System Overview
Four stages:
reconnaissance
reasoning
acquisition
validation
Aims to be autonomous, might require human supervision.
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9. Reconnaissance stage - Hardware setup
robotic platform
organic bipedal
platform
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10. Reconnaissance stage - Robotic platform
stable platform
limited indoor access
needs pitch and roll
corrections on uneven
terrain
cannot traverse stairs
internal power supply
robotic platform
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11. Reconnaissance stage - Organic bipedal platform
unstable platform
requires constant pitch
and roll corrections
go-anywhere ability
requires external power
supply for equipement
allowed inside historical
monuments
runs off chips and coffe
organic bipedal
platform
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12. Environment Mapping - Occlusions
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13. Environment Mapping - Occlusions
Occlusion detection types:
from single viewpoint using movement
multiple viewpoints using optical flow
multiple viewpoints using object correlation
Assumptions:
static environment
known position of the viewpoint
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14. Reasononing stage
Detection of laser scanner positions
User supervision:
provide the desired resolution based on map visualisation
provide optimisation parameters
confirm that scanner positions are accessible
Automation:
based on optimisation parameters
using the occlusion map
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15. Acquisition stage - Robotic platform
Acquisition:
Use of HDS6200 laser scanner
Robotic platform will traverse to
the accessible scanning locations
Fast
Stable platform
No supervision
Limited accessibility
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16. Acquisition stage - Human
Acquisition:
Use of HDS6200 laser scanner
Human operator will place a
tripod in less accessible locations
Requires manual tripod leveling
Can get to the hard-to-reach
places
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17. Validation stage
Validation:
Detection of potenetial registration targets
Estimation of registration error
Estimation on registration completeness
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18. Finally the last slide !
Any questions?
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