# RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration

## TL;DR

This paper introduces a robust multi-sensor navigation system combining vision, IMU, and UWB for improved accuracy in real-world scenarios.

## Contribution

The novel adaptive robust factor graph model estimates UWB ranging covariance in real-time without prior calibration.

## Key findings

- The proposed system outperforms state-of-the-art methods in all tested scenarios.
- RMSE improvements of up to 82.15% are achieved in non-line-of-sight environments.
- The model adapts to dynamic sensor availability and observation weight distribution issues.

## Abstract

High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a “plug and play” Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightly-coupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment).