Analytical SLAM Without Linearization

TL;DR

This paper introduces a nonlinear SLAM algorithm that avoids linearization errors by using virtual measurements and a linear time-varying Kalman observer, ensuring guaranteed convergence in 2D and 3D scenarios.

Contribution

It presents a novel SLAM method that eliminates linearization approximations, leveraging virtual measurements and contraction analysis for guaranteed convergence.

Findings

Successfully solves 2D and 3D SLAM problems.

Guarantees convergence without linearization errors.

Compatible with various sensor types.

Abstract

This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion which avoids linearized approximations altogether. Based on creating virtual synthetic measurements, the algorithm uses a linear time- varying (LTV) Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering (EKF) SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, or time-to-contact. As illustrated in simulations, the proposed algorithm can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.