# Visual-Inertial Mapping with Non-Linear Factor Recovery

## TL;DR

This paper introduces a novel method for visual-inertial mapping that reconstructs non-linear factors from odometry data, enhancing global consistency and accuracy in environment mapping.

## Contribution

It proposes a non-linear factor recovery approach from visual-inertial odometry to improve global mapping robustness and accuracy.

## Key findings

- Outperforms state-of-the-art methods on public benchmarks.
- Enhances global map consistency through loop closure integration.
- Improves orientation observability with VIO factors.

## Abstract

Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.