# AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry

arXiv: https://arxiv.org/abs/2410.05996 · HTML: https://mlsanity.com/paper/2410.05996/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [Vision-based Goal-Reaching Control for Mobile Robots Using a Hierarchical Learning Framework](https://mlsanity.com/paper/2601.00610/) (2026)
- [Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation](https://mlsanity.com/paper/2509.01583/) (2025)
- [HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames](https://mlsanity.com/paper/2509.19452/) (2025)

## Abstract (arXiv)

Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic information about the objects of interest from raw sensory data. While deep learning-based (DL) methods excel at inferring semantic object information from images, such as class and relative 6 degree of freedom (6-DoF) pose, they are computationally demanding and thus often not suitable for payload constrained mobile robots. In this letter we present a real-time capable unmanned aerial vehicle (UAV) system for object-relative, closed-loop navigation with a minimal sensor configuration consisting of an inertial measurement unit (IMU) and RGB camera. Utilizing a DL-based object pose estimator, solely trained on synthetic data and optimized for companion board deployment, the object-relative pose measurements are fused with the IMU data to perform object-relative localization. We conduct multiple real-world experiments to validate the performance of our system for the challenging use case of power pole inspection. An example closed-loop flight is presented in the supplementary video.