Optimisation of laser data acquisition: Robots and lasers

394 views

Published on

Marek Ososinski, Aberystwyth University

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
394
On SlideShare
0
From Embeds
0
Number of Embeds
21
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Optimisation of laser data acquisition: Robots and lasers

  1. 1. Optimisation of laser data acquisition Robots and lasers Marek Ososinski Aberystwyth UniversityMarek Ososinski (Aberystwyth University) Robots and lasers 1 / 18
  2. 2. Layout 1 Introduction 2 Project Desription 3 System overview 4 Environment Mapping 5 Reasoning, Acquisition and ValidationMarek Ososinski (Aberystwyth University) Robots and lasers 2 / 18
  3. 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 WalesMarek Ososinski (Aberystwyth University) Robots and lasers 3 / 18
  4. 4. A bit of historyMarek Ososinski (Aberystwyth University) Robots and lasers 4 / 18
  5. 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.Marek Ososinski (Aberystwyth University) Robots and lasers 5 / 18
  6. 6. General idea to here getting from hereMarek Ososinski (Aberystwyth University) Robots and lasers 6 / 18
  7. 7. Optimisation of laser data acquisition Optimisation factors: Time Registration error Scan completeness Processing effortMarek Ososinski (Aberystwyth University) Robots and lasers 7 / 18
  8. 8. System Overview Four stages: reconnaissance reasoning acquisition validation Aims to be autonomous, might require human supervision.Marek Ososinski (Aberystwyth University) Robots and lasers 8 / 18
  9. 9. Reconnaissance stage - Hardware setup robotic platform organic bipedal platformMarek Ososinski (Aberystwyth University) Robots and lasers 9 / 18
  10. 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 platformMarek Ososinski (Aberystwyth University) Robots and lasers 10 / 18
  11. 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 platformMarek Ososinski (Aberystwyth University) Robots and lasers 11 / 18
  12. 12. Environment Mapping - OcclusionsMarek Ososinski (Aberystwyth University) Robots and lasers 12 / 18
  13. 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 viewpointMarek Ososinski (Aberystwyth University) Robots and lasers 13 / 18
  14. 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 mapMarek Ososinski (Aberystwyth University) Robots and lasers 14 / 18
  15. 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 accessibilityMarek Ososinski (Aberystwyth University) Robots and lasers 15 / 18
  16. 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 placesMarek Ososinski (Aberystwyth University) Robots and lasers 16 / 18
  17. 17. Validation stage Validation: Detection of potenetial registration targets Estimation of registration error Estimation on registration completenessMarek Ososinski (Aberystwyth University) Robots and lasers 17 / 18
  18. 18. Finally the last slide ! Any questions?Marek Ososinski (Aberystwyth University) Robots and lasers 18 / 18

×