Mark B. Milam — accepted AI-conference papers

2021

Learned Uncertainty Calibration for Visual Inertial Localization

Stephanie Tsuei, Stefano Soatto, Paulo Tabuada, Mark B. Milam

ICRA 2021poster

The widely-used Extended Kalman Filter (EKF) provides a straightforward recipe to estimate the mean and covariance of the state given all past measurements in a causal and recursive fashion. For a wide variety of applications, the EKF is known to produce accurate estimates of the mean and typically…

Cited by 4 Source ↗Scholar ↗

2017

The Matroid Team Surviving Orienteers problem: Constrained routing of heterogeneous teams with risky traversal

Stefan Jorgensen, Robert H. Chen, Mark B. Milam, Marco Pavone

IROS 2017poster

Consider a setting where robots must visit sites represented as nodes in a graph, but each robot may fail when traversing an edge. The goal is to find a set of paths for a team of robots which maximizes the expected number of nodes collectively visited, while guaranteeing that the paths satisfy a no…

Cited by 29 Source ↗Scholar ↗