Fast Adaptation Nonlinear Observer for SLAM

TL;DR

This paper introduces a nonlinear observer for SLAM that adapts quickly and compensates for measurement biases, improving robustness in 3D environment mapping and vehicle localization.

Contribution

A novel nonlinear SLAM observer modeled on Lie groups that enables fast adaptation and bias compensation, advancing current SLAM methodologies.

Findings

Simulation results demonstrate robustness of the proposed observer.

The observer effectively compensates for unknown velocity biases.

Fast adaptation improves SLAM performance in dynamic environments.

Abstract

The process of simultaneously mapping the environment in three dimensional (3D) space and localizing a moving vehicle's pose (orientation and position) is termed Simultaneous Localization and Mapping (SLAM). SLAM is a core task in robotics applications. In the SLAM problem, each of the vehicle's pose and the environment are assumed to be completely unknown. This paper takes the conventional SLAM design as a basis and proposes a novel approach that ensures fast adaptation of the nonlinear observer for SLAM. Due to the fact that the true SLAM problem is nonlinear and is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, the proposed observer for SLAM is nonlinear and modeled on $\mathbb{SLAM}_{n}\left(3\right)$. The proposed observer compensates for unknown bias attached to velocity measurements. The results of the simulation illustrate the robustness of the proposed approach.