The Geometry of Navigation Problems

TL;DR

This paper introduces a systematic method to construct group structures for navigation systems, enabling invariant filtering techniques like IEKF to be applied more broadly and effectively, including for systems with biases and new applications.

Contribution

A novel methodology for deriving group structures for a wide class of navigation systems, enhancing invariant filtering and extending its applicability.

Findings

Improves state estimation in inertial navigation with sensor biases.

Enables application of invariant filtering to new navigation problems.

Provides a unifying framework for systems previously incompatible with IEKF.

Abstract

While many works exploiting an existing Lie group structure have been proposed for state estimation, in particular the Invariant Extended Kalman Filter (IEKF), few papers address the construction of a group structure that allows casting a given system into the framework of invariant filtering. In this paper we introduce a large class of systems encompassing most problems involving a navigating vehicle encountered in practice. For those systems we introduce a novel methodology that systematically provides a group structure for the state space, including vectors of the body frame such as biases. We use it to derive observers having properties akin to those of linear observers or filters. The proposed unifying and versatile framework encompasses all systems where IEKF has proved successful, improves state-of-the art "imperfect" IEKF for inertial navigation with sensor biases, and allows addressing novel examples, like GNSS antenna lever arm estimation.