Random Fourier Features based SLAM

TL;DR

This paper introduces a novel SLAM algorithm using Gaussian Processes with Random Fourier Features, enabling broader kernel choices and efficient computation, demonstrated to outperform existing methods on benchmarks.

Contribution

It presents a RFF-based GP SLAM method that overcomes kernel restrictions and maintains low computational complexity, improving accuracy and flexibility.

Findings

Outperforms state-of-the-art on synthetic benchmarks.

Allows broader kernel functions in GP-based SLAM.

Maintains low computational complexity with RFF.

Abstract

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.