Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT Navigation

TL;DR

This paper introduces a novel visual-inertial odometry method that effectively uses long-tracked features with an active error decoupling mechanism, achieving high-accuracy low-altitude IoT navigation in real-time on edge devices.

Contribution

It proposes an active decoupling mechanism, reference frame reset, and depth prediction strategies to improve long-tracked feature utilization in VIO, enhancing accuracy and efficiency.

Findings

Higher positioning accuracy demonstrated on various datasets.

Real-time performance achieved suitable for edge-enabled IoT navigation.

Effective error mitigation strategies improve long-term feature tracking.

Abstract

This paper presents a visual-inertial odometry (VIO) method using long-tracked features. Long-tracked features can constrain more visual frames, reducing localization drift. However, they may also lead to accumulated matching errors and drift in feature tracking. Current VIO methods adjust observation weights based on re-projection errors, yet this approach has flaws. Re-projection errors depend on estimated camera poses and map points, so increased errors might come from estimation inaccuracies, not actual feature tracking errors. This can mislead the optimization process and make long-tracked features ineffective for suppressing localization drift. Furthermore, long-tracked features constrain a larger number of frames, which poses a significant challenge to real-time performance of the system. To tackle these issues, we propose an active decoupling mechanism for accumulated errors in long-tracked feature utilization. We introduce a visual reference frame reset strategy to eliminate accumulated tracking errors and a depth prediction strategy to leverage the long-term constraint. To ensure real time preformane, we implement three strategies for efficient system state estimation: a parallel elimination strategy based on predefined elimination order, an inverse-depth elimination simplification strategy, and an elimination skipping strategy. Experiments on various datasets show that our method offers higher positioning accuracy with relatively short consumption time, making it more suitable for edge-enabled low-altitude IoT navigation, where high-accuracy positioning and real-time operation on edge device are required. The code will be published at github.