ガウス混合モデルを用いた航空機の高速モンテカルロ自己位置推定
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
深度センサの点群をガウス混合モデルで圧縮表現し、パーティクルフィルタで高速な自己位置推定を実現、組み込み環境でも従来手法を上回る性能を示した。
著者: Aditya Dhawale, Kumar Shaurya Shankar, Nathan Michael
分類: cs.RO
原文アブストラクト
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-the-art algorithm on the same environment.
