# An Improved Robust ESKF Fusion Positioning Method with a Novel UWB-VIO Initialization

## TL;DR

The paper introduces a new UWB-VIO localization method for mobile robots that improves accuracy and robustness in challenging indoor environments.

## Contribution

A novel UWB-VIO initialization model and a robust ESKF fusion method that suppresses UWB interference without external calibration.

## Key findings

- The direction-consistent initialization model aligns VIO and UWB frames without external calibration.
- The improved robust ESKF fusion reduces UWB-induced drift and enhances localization stability.
- Experiments show over 50% better accuracy in narrow corridors with severe UWB interference.

## Abstract

What are the main findings?
A direction-consistent constrained initialization model is proposed to jointly optimize scale and heading, achieving consistent alignment between VIO and UWB coordinate frames without external calibration.An improved residual-weighted robust ESKF fusion method adaptively suppresses UWB multipath and NLOS-induced outliers, effectively reducing VIO drift and enhancing localization robustness.

A direction-consistent constrained initialization model is proposed to jointly optimize scale and heading, achieving consistent alignment between VIO and UWB coordinate frames without external calibration.

An improved residual-weighted robust ESKF fusion method adaptively suppresses UWB multipath and NLOS-induced outliers, effectively reducing VIO drift and enhancing localization robustness.

What are the implications of the main findings?
The proposed UWB–VIO framework enables high-precision and stable localization for mobile robots in complex indoor environments with illumination variations and feature sparsity.The findings provide a practical and robust localization solution for autonomous navigation and mapping in GNSS-denied scenarios.

The proposed UWB–VIO framework enables high-precision and stable localization for mobile robots in complex indoor environments with illumination variations and feature sparsity.

The findings provide a practical and robust localization solution for autonomous navigation and mapping in GNSS-denied scenarios.

Visual–inertial odometry (VIO) often struggles with illumination variations, sparse visual features, and inertial drift in complex indoor settings, leading to scale uncertainties and accumulated errors. To address these issues, this paper proposes a new UWB–VIO initialization method combined with an enhanced Robust error-state Kalman filter (Robust ESKF) fusion technique for mobile robot localization. During initialization, common problems include scale drift and heading inconsistency. To solve these, a direction-consistent constrained initialization model is developed. By jointly optimizing the scale factor and yaw angle, this model ensures consistent alignment between the visual–inertial and ultra-wideband (UWB) coordinate frames. This approach removes the need for external calibration and independent coordinate transformation, which are typically required by traditional methods. In the fusion process, an improved residual-weighted robust filtering mechanism is employed to minimize the impact of abnormal UWB ranging data and noise interference. This mechanism adaptively suppresses outliers caused by UWB multipath reflections and non-line-of-sight (NLOS) propagation, thereby reducing VIO drift and improving the overall robustness and stability of the localization system. Experiments conducted in narrow-corridor environments, where both UWB and visual sensors are affected by interference, demonstrate that the proposed method significantly reduces trajectory drift and attitude jumps, resulting in better positioning accuracy and trajectory continuity. Compared to conventional UWB–VIO fusion algorithms, the proposed method enhances average localization accuracy by over 50% and maintains stable estimation even in severe multipath interference conditions, demonstrating high precision and strong robustness.