# Keyframe-Based Visual-Inertial Online SLAM with Relocalization

## TL;DR

This paper introduces a real-time keyframe-based visual-inertial SLAM system that achieves global consistency, relocalization, and outperforms existing methods in accuracy and efficiency on benchmark datasets.

## Contribution

It presents a novel keyframe-based visual-inertial SLAM approach with online loop closure and relocalization capabilities, enhancing robustness and accuracy.

## Key findings

- State-of-the-art accuracy in trajectory estimation
- Effective relocalization in previously mapped environments
- Real-time performance on benchmark datasets

## Abstract

Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our system supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public indoor benchmark datasets and on newly recorded outdoor sequences. We demonstrate state-of-the-art performance of our system compared to a visual-inertial odometry method and baseline visual SLAM approaches in recovering the trajectory of the camera.