Fast Monte-Carlo Localization on Aerial Vehicles using
Approximate Continuous Belief Representations
Abstract
Size, weight, and power constrained platforms impose constraints on computational resources that introduce
unique challenges in implementing localization algorithms.
We present a framework to perform fast localization on such
platforms enabled by the compressive capabilities of Gaussian Mixture Model representations of point cloud data.
Given raw structural data from a depth sensor and pitch
and roll estimates from an on-board attitude reference system, a multi-hypothesis particle filter localizes the vehicle
by exploiting the likelihood of the data originating from the
mixture model. We demonstrate analysis of this likelihood
in the vicinity of the ground truth pose and detail its utilization in a particle filter-based vehicle localization strategy,
and later present results of real-time implementations on a
desktop system and an off-the-shelf embedded platform that
outperform localization results from running a state-of-theart algorithm on the same environment.