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Size, weight, and power constrained platforms impose constraints oncomputational resources that introduce unique challenges in implementinglocalization algorithms. We present a framework to perform fast localization onsuch platforms enabled by the compressive capabilities of Gaussian MixtureModel representations of point cloud data. Given raw structural data from adepth sensor and pitch and roll estimates from an on-board attitude referencesystem, a multi-hypothesis particle filter localizes the vehicle by exploitingthe likelihood of the data originating from the mixture model. We demonstrateanalysis of this likelihood in the vicinity of the ground truth pose and detailits utilization in a particle filter-based vehicle localization strategy, andlater present results of real-time implementations on a desktop system and anoff-the-shelf embedded platform that outperform localization results fromrunning a state-of-the-art algorithm on the same environment.

Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations