Fast Monte-Carlo Localization on Aerial Vehicles Using Approximate Continuous Belief Representations
Fast Monte-Carlo Localization on Aerial Vehicles Using Approximate Continuous Belief Representations
复制标题
使用近似连续置信表示的飞行器快速蒙特卡罗定位
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Nathan Michael
中科院分区：
文献类型：
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作者：
A. Dhawale;Kumar Shaurya Shankar;Nathan Michael
尺寸、重量和功率受限的平台对计算资源施加了约束，这在实现定位算法时引入了独特的挑战。我们提出了一个框架，以执行快速本地化的点云数据的高斯混合模型表示的压缩能力，使这样的平台。给定来自深度传感器的原始结构数据和来自机载姿态参考系统的俯仰和滚转估计，多假设粒子滤波器通过利用源自混合模型的数据的可能性来定位车辆。我们展示了这种可能性的分析在附近的地面实况姿态和详细的利用基于粒子滤波器的车辆定位策略，并随后提出了实时实现的结果在桌面系统和现成的嵌入式平台上运行一个国家的最先进的算法在相同的环境中优于本地化的结果。
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.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram