Sensing the Physical World
Capturing three-dimensional data of the surface topography of real objects (3D
Scanning) is the basis for modern automated 3D modelling methods. 3D scanning
is also the basis of computer vision methods for autonomous agents
understanding and acting within real world environments. Single sensor scanning
systems produce 2.5D data and require relative motion of the sensor or scene to
obtain 3D data.
This research investigates the simultaneous use of multiple, inexpensive 2.5D
Kinect(TM) sensors for the purpose of capturing 3D topographic data of a static
scene. In particular, the problems of sensor calibration and data registration are
investigated to determine appropriate methods for overcoming known sources
of error in stereo vision problems. Previously reported interference effects
between multiple Structured Light depth sensors are also investigated and found
to be mitigated in overlapping point clouds. The results of this research are a
system design (including algorithms) for a multi-Kinect 3D scanning system.
Validation of this sensor system was performed by scanning mannequin heads,
which successfully produced non-occluded point cloud models of the face. These
results are significant as they demonstrate a new capacity to capture accurate
colour and topographic data for novel applications such as 3D facial recognition.
 Lei Pan, Xi Zheng, Philip Kolar, and Shaun Bangay. Object localization through clustering unreliable ultrasonic range sensors. International Journal of Sensor Networks, 27(4), 2018. [PDF] [BibTeX]
 Cameron Starkey. Spatial sound for representing the location of virtual objects. Technical Report Honours Project Report, GIVE group, School of Information Technology, Deakin University, Australia, October 2015. [PDF] [BibTeX]
 Philip Kolar. Single target tracking in range-only directional sensor networks. Technical Report Honours Project Report, GIVE group, School of Information Technology, Deakin University, Australia, October 2014. [PDF] [BibTeX]