ADUGS-VINS: Generalized Visual-Inertial Odometry for Robust Navigation in Highly Dynamic and Complex Environments

TL;DR

ADUGS-VINS enhances visual-inertial odometry by integrating advanced algorithms and foundation models, significantly improving pose estimation accuracy in dynamic, occluded environments across multiple real-world scenarios.

Contribution

The paper introduces ADUGS-VINS, a novel VIO method combining an improved SORT algorithm and a promptable foundation model for better dynamic object handling.

Findings

Outperforms state-of-the-art methods in diverse scenarios

Demonstrates robustness in environments with occlusions and dynamic objects

Shows strong generalization and adaptability in real-world tests

Abstract

Visual-inertial odometry (VIO) is widely used in various fields, such as robots, drones, and autonomous vehicles. However, real-world scenes often feature dynamic objects, compromising the accuracy of VIO. The diversity and partial occlusion of these objects present a tough challenge for existing dynamic VIO methods. To tackle this challenge, we introduce ADUGS-VINS, which integrates an enhanced SORT algorithm along with a promptable foundation model into VIO, thereby improving pose estimation accuracy in environments with diverse dynamic objects and frequent occlusions. We evaluated our proposed method using multiple public datasets representing various scenes, as well as in a real-world scenario involving diverse dynamic objects. The experimental results demonstrate that our proposed method performs impressively in multiple scenarios, outperforming other state-of-the-art methods. This highlights its remarkable generalization and adaptability in diverse dynamic environments, showcasing its potential to handle various dynamic objects in practical applications.