论文标题
基于随机傅立叶功能的大满贯
Random Fourier Features based SLAM
论文作者
论文摘要
这项工作致力于基于高斯流程（GP）的同时连续时间轨迹估计和映射。基于最先进的GP同时定位和映射模型（SLAM）是计算上有效的，但只能与受限类的内核函数一起使用。本文提供了基于GP的算法，具有随机傅立叶特征（RFF）的近似值，而没有任何约束。 RFF在连续时间大满贯中的优点是，我们可以考虑一类更广泛的内核，同时通过在功能的傅立叶空间中运行，以合理较低的水平保持计算复杂性。精度速度权衡可以由功能数量控制。我们对合成和现实基准测试的实验结果证明了与当前最新技术相比，我们的方法提供了更好的结果。
This work is dedicated to simultaneous continuous-time trajectory estimation and mapping based on Gaussian Processes (GP). State-of-the-art GP-based models for Simultaneous Localization and Mapping (SLAM) are computationally efficient but can only be used with a restricted class of kernel functions. This paper provides the algorithm based on GP with Random Fourier Features (RFF) approximation for SLAM without any constraints. The advantages of RFF for continuous-time SLAM are that we can consider a broader class of kernels and, at the same time, maintain computational complexity at reasonably low level by operating in the Fourier space of features. The accuracy-speed trade-off can be controlled by the number of features. Our experimental results on synthetic and real-world benchmarks demonstrate the cases in which our approach provides better results compared to the current state-of-the-art.
