Estimation for autonomous mobile robot navigation
localization and mapping
localization for mobile sensor networks
sensing and estimation under processing and communication
My Ph. D. thesis focuses on resource-aware
estimation for single- and multi-robot localization/mapping. In
the presence of time-varying processing and communication-bandwidth
constraints, the objective of my work is to design estimators
that can optimally utilize these time-varying resources in order to
maximize estimation accuracy.
2013: I am back to working on SLAM! Specifically, I am working on batch
MAP estimators (commonly known as bundle adjustment) using inertial
sensors and cameras and/or depth sensors. We are currently
developing a novel and promising keyframe-based batch-MAP approach
called C-KLAM (Constrained Keyframe Localization and Mapping) for
real-time localization and mapping. We will be presenting our
preliminary results at the "Long-Term Autonomy: Navigation and Mapping for Real-World Applications" workshop on 10 May 2013 at ICRA in Karlsruhe, Germany.
2013: Currently, I am working on Covariance Intersection algorithms for
multi-robot cooperative localization. Our proposed algorithm, submitted
to IROS 2013, can handle both limited processing resources and
asynchronous communication constraints.
Dec 2012 : Recently, I have been working on the extremely interesting
and challenging problem of designing estimators capable of
processing a combination of analog and quantized measurements.
The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the University of Minnesota.