Tom Stone PhD

Thomas Stone

Research Interests

I am currently creating a spiking neural model of a central brain region found in all insects called the central complex (CX). This area is interesting as it is both highly implicated in navigational behaviour and physiologically remarkably consistent across many different species of insects that are sometimes evolutionarily separated by over 360 million years. My model is based on data collected from the desert locust Schistocerca Gregaria. In particular, I am looking at neural responses to differently polarised light sources in the visual pathway, and attempting to reconstruct the polarisation coding network.

  Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics
Stone, T, Differt, D, Milford, M & Webb, B 2016, Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 5615-5622, 2016 IEEE International Conference on Robotics and Automation, Stockholm, Sweden, 16/05/16. DOI: 10.1109/ICRA.2016.7487780
Place recognition is a key capability for navigating robots. While significant advances have been achieved on large, stable platforms such as robot cars, achieving robust performance on rapidly manoeuvring platforms in outdoor natural conditions remains a challenge, with few systems able to deal with both variable conditions and large tilt variations caused by rough terrain. Taking inspiration from biology, we propose a novel combination of sensory modality and image processing to obtain a significant improvement in the robustness of sequence-based image matching for place recognition. We use a UV-sensitive fisheye lens camera to segment sky from ground, providing illumination invariance, and encode the resulting binary images using spherical harmonics to enable rotation-invariant image matching. In combination, these methods also produce substantial pitch and roll invariance, as the spherical harmonics for the sky shape are minimally affected, providing the sky remains visible. We evaluate the performance of our method against a leading appearance-invariant technique (SeqSLAM) and a leading viewpoint-invariant technique (FAB-MAP 2.0) on three new outdoor datasets encompassing variable robot heading, tilt, and lighting conditions in both forested and urban environments. The system demonstrates improved condition- and tilt-invariance, enabling robust place recognition during aggressive zigzag manoeuvring along bumpy trails and at tilt angles of up to 60 degrees.
General Information
Organisations: School of Informatics.
Authors: Stone, T., Differt, D., Milford, M. & Webb, B..
Number of pages: 8
Pages: 5615-5622
Publication Date: 1 May 2016
Publication Information
Category: Conference contribution
Original Language: English
  Sky segmentation with ultraviolet images can be used for navigation
Stone, T, Mangan, M, Ardin, P & Webb, B 2014, Sky segmentation with ultraviolet images can be used for navigation. in Proceedings Robotics: Science and Systems.
Inspired by ant navigation, we explore a method for
sky segmentation using ultraviolet (UV) light. A standard camera
is adapted to allow collection of outdoor images containing light
in the visible range, in UV only and in green only. Automatic
segmentation of the sky region using UV only is signi?cantly more
accurate and far more consistent than visible wavelengths over
a wide range of locations, times and weather conditions, and can
be accomplished with a very low complexity algorithm. We apply
this method to obtain compact binary (sky vs non-sky) images
from panoramic UV images taken along a 2km route in an urban
environment. Using either sequence SLAM or a visual compass
on these images produces reliable localisation and orientation
on a subsequent traversal of the route under different weather
General Information
Organisations: Neuroinformatics DTC.
Authors: Stone, Thomas, Mangan, Michael, Ardin, Paul & Webb, Barbara.
Number of pages: 9
Publication Date: 2014
Publication Information
Category: Chapter (peer-reviewed)
Original Language: English

Modelling the central complex (PhD)