Know What You Don't Know: Consistency in Sliding Window Filtering with Unobservable States Applied to Visual-Inertial SLAM (Extended Version)

TL;DR

This paper addresses the challenge of maintaining consistency in sliding window filters for visual-inertial SLAM, especially regarding unobservable states, by proposing general requirements and demonstrating their effectiveness experimentally.

Contribution

It introduces general requirements for consistency in sliding window filters with unobservable states and validates them within visual-inertial SLAM using IMU preintegration.

Findings

Proposed requirements improve filter consistency in SLAM.

Experimental validation shows enhanced state estimation accuracy.

Framework applicable to various navigation algorithms.

Abstract

Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to ``know what it doesn't know'', meaning that it must maintain the unobservable states as unobservable during algorithm deployment. This letter presents general requirements for maintaining consistency in sliding window filters involving unobservable states. The value of these requirements for designing navigation solutions is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.