DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery

TL;DR

DynaVINS++ introduces a robust visual-inertial navigation system that effectively handles abrupt dynamic objects through adaptive truncation and stable state recovery, improving accuracy and preventing divergence in dynamic environments.

Contribution

It proposes a novel VINS framework with adaptive truncation and bias correction to enhance robustness against abruptly dynamic objects.

Findings

Effective in dynamic scenes with moving objects

Reduces divergence caused by dynamic objects

Outperforms existing methods in real-world tests

Abstract

Despite extensive research in robust visual-inertial navigation systems~(VINS) in dynamic environments, many approaches remain vulnerable to objects that suddenly start moving, which are referred to as \textit{abruptly dynamic objects}. In addition, most approaches have considered the effect of dynamic objects only at the feature association level. In this study, we observed that the state estimation diverges when errors from false correspondences owing to moving objects incorrectly propagate into the IMU bias terms. To overcome these problems, we propose a robust VINS framework called \mbox{\textit{DynaVINS++}}, which employs a) adaptive truncated least square method that adaptively adjusts the truncation range using both feature association and IMU preintegration to effectively minimize the effect of the dynamic objects while reducing the computational cost, and b)~stable state recovery with bias consistency check to correct misestimated IMU bias and to prevent the divergence caused by abruptly dynamic objects. As verified in both public and real-world datasets, our approach shows promising performance in dynamic environments, including scenes with abruptly dynamic objects.