The document describes Marek Ososinski's PhD project to develop a robotic system for optimizing laser data acquisition. The system will map an environment, detect optimal scanning positions to minimize registration error while ensuring completeness, autonomously acquire laser scans, and validate the results. It focuses on balancing time, registration error, completeness, and processing effort. The system involves reconnaissance using a robotic or human-operated platform, reasoning about positions, autonomous or manual data acquisition, and validation.

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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