Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
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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 coarse 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 and detail its utilization in a particle filter-based vehicle localization strategy, and later present results of real-time implementations on a commercial off-the-shelf embedded platform that outperform localization results from running a state-of-the-art algorithm, ORB-SLAM2, on the same environment.
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