wits presentation 3_02062015

Scene Identification based onConcept LearningBeatrice van Eden

- Part time PhD Student at the University of the Witwatersrand.- Fulltime employee of the Council for Scientific and Industrial Research.

Index• Broad problem statement Give an overview of the researchI am busy with. • Identify items in a scene 3D Current work on the project.

• Continued workHighlights of some of the reading I did.

Broad Problem Statement

• How can we give a mobile robot the capability to continuously and autonomously form concepts of its environment?

Identify items in a scene 3D

• Marius Muja of University of British Columbia (2009)• Manipulating tabletop objects: one of the main tasks of a robot in

a household environment• Setting/cleaning the table• Serving drinks

• Usual tabletop objects are difficult (no texture, transparent)• Techniques based on local features fail• RANSAC – Random sample consensus (voting scheme to find optima fitting result)

Identify items in a scene 3D • Grasping of objects requires precise object localization• Bounding box around the object is not enough• Know the grasp

• Extended the 2D chamfer matching approach to 3D• Chamfer – calculates distance between two images

Identify items in a scene 3D • Two stage approach• Bottom-up object localization

• Determines probable object locations• Table plane detection and removal• Point cloud clustering

• Top down model fitting• Determines exact object pose and identity• Find the model with the best correspondence to the point cloud• ICP-like (Iterative Closest Point) algorithm

Identify items in a scene 3D • \tabletop objects" package• The 3D model fitter determines

• Object identity• Object pose• Grasp pose• Object mesh - used in the planning stage for a more precise collision

map• Integrated with the planning pipeline

Continued work• Backtracks a bit.• Go back to the technical sections of papers I already read• Go through the pseudo code of the work in this papers• Test one or two more things I am unsure about in the 2D object

detection I showed you last week.

Thank you