Observability and Consistency Analysis for Visual-Inertial Navigation with Anchored Feature Parameterizations

2026-06-17

AI Summary

This work addresses the observability and consistency issues in filter-based visual-inertial navigation systems (VINS) under anchored feature parameterization. By analyzing the unobservable subspace, the study reveals that it depends solely on the navigation state and is independent of landmark parameters. Building on this insight, two novel mechanisms are proposed to enhance estimation consistency without altering the underlying system structure. Experimental evaluations on both simulated data and the TUM-VI dataset demonstrate that the proposed methods significantly improve consistency, achieving performance comparable to state-of-the-art consistency-preserving approaches that rely on global feature representations.

Abstract

This paper presents an analysis of the observability and consistency properties of filtering-based visual-inertial navigation systems (VINS) that utilize anchored feature representations. The unobservable subspace of VINS with anchored landmark parameterizations is shown to be independent of the estimated landmark state, which leads to improved estimator consistency properties without any additional modifications. However, the unobservable subspace is still found to depend on the estimated navigation state, necessitating additional consistency-enforcing techniques. Two methods to improve the consistency of VINS with anchored feature representations are presented. Simulation results showcase that all estimators employing anchored feature paramterizations exhibit improved consistency properties compared to algorithms that estimate features resolved in a global reference frame, especially in scenarios where feature initialization may be poor. Real-world experiments on the TUM-VI dataset showcase that the use of anchored feature representations alone can yield comparable performance to consistency-improved estimators employing a global feature representation, demonstrating the benefit of using anchored feature parameterizations for VINS.

Problem

• visual-inertial navigation
• anchored feature parameterization
• observability
• consistency
• state estimation

Innovation

• anchored feature parameterization
• visual-inertial navigation
• observability analysis
• estimator consistency
• filtering-based VINS