Lee Clement

Ph.D. Student
Department:

As robotics enters the “robust perception age”, a major focus of modern robotics research will be the design of perception systems capable of operating over extended periods of time in a broad range of environments. Visual perception in particular holds great promise in this area due to the wealth of information available from standard colour cameras. Indeed, we humans rely heavily on vision for navigating our daily lives. But how can we use vision to build persistent maps and localize against them when the appearance of the world is always changing?

 

Lee’s research focuses on developing ways for robots to reason about more than just the geometry of their environment by incorporating information about illumination and appearance into the mapping and localization problem. In particular, he is interested in using machine learning algorithms to create robust data-driven models of visual appearance, and using these models as an enabler of long-term visual navigation.

 

Projects he has worked on include:

 

 

Appearance Modelling for Long-term Visual Localization


CAT-Net learns to transform images to correspond to a previously-seen reference appearance.

Visual Sun Sensing


Sun-BCNN regresses the 3D direction of the sun to improve stereo VO.

Inferring sun direction to improve visual odometry: A deep learning approach
Valentin Peretroukhin, Lee Clement, and Jonathan Kelly
IJRR 2018.
Reducing Drift in Visual Odometry by Inferring Sun Direction using a Bayesian Convolutional Neural Network
Valentin Peretroukhin, Lee Clement, and Jonathan Kelly
ICRA 2017. Singapore.
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
Lee Clement, Valentin Peretroukhin, and Jonathan Kelly
ISER 2016. Tokyo, Japan.

Monocular Visual Teach & Repeat


MonoVT&R is capable of retracing human-taught routes with centimetre accuracy using only a monocular camera.

Robust Monocular Visual Teach and Repeat Aided by Local Ground Planarity and Colour-Constant Imagery
Lee Clement, Jonathan Kelly, and Timothy D. Barfoot
JFR 2017.
Monocular Visual Teach and Repeat Aided by Local Ground Planarity
Lee Clement, Jonathan Kelly, and Timothy D. Barfoot
FSR 2015. Toronto, Canada.