Work

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

Year: 2024

Type: article

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 asabruptly 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 calledDynaVINS++, 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.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2024.3455905

Authors: Seungwon Song, Hyungtae Lim, Alex Junho Lee, Hyun Myung

Institutions: Hyundai Motor Group (South Korea), Hyundai Motors (South Korea), Massachusetts Institute of Technology, Korea Advanced Institute of Science and Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green