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Resolution and Frequency Effects on UAVs Semi-Direct Visual-Inertial Odometry (SVO) for Warehouse Logistics.

Simone Godio¹, Adrian Carrio², Giorgio Guglieri¹

¹Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10128 Turin, Italy.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

Optimizing warehouse navigation, this study tested the SVO Pro Open visual-inertial odometry algorithm. Best performance for safe navigation was achieved at 636x600 px resolution, with localization errors under 0.25m and CPU usage below 60%.

Keywords:

ROS SVO aerial system autonomous localization frequency indoor localization resolution visual inertial odometry warehouse

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Area of Science:

Robotics and Automation
Computer Vision
Industrial Engineering

Background:

Warehouses are critical for commerce, necessitating efficiency improvements and cost reduction.
Inventory management is a time-intensive process impacting company revenue.
Accurate localization and navigation are essential for optimizing warehouse operations.

Purpose of the Study:

To analyze the performance of the SVO Pro Open visual-inertial odometry algorithm in an industrial warehouse setting.
To determine the optimal video streaming resolution and frequency for efficient warehouse navigation.
To evaluate the trade-offs between localization accuracy, robustness, and computational cost.

Main Methods:

The SVO Pro Open algorithm's performance was evaluated by varying video streaming resolution and frequency.
Multiple resolutions were tested with a constant aspect ratio, each requiring precise calibration.
Localization accuracy, system robustness, and Central Processing Unit (CPU) utilization were monitored.

Main Results:

A stable operating point was identified, balancing robustness, localization accuracy, and CPU load.
The optimal resolution of 636 × 600 px yielded localization errors (x, y, z) below 0.25 meters.
Central Processing Unit (CPU) usage remained under 60%, allowing for additional onboard processing.

Conclusions:

The SVO Pro Open algorithm demonstrates effective performance for safe warehouse navigation.
Adjusting video resolution is a key factor in optimizing visual-inertial odometry for industrial environments.
The findings provide a foundation for developing efficient and cost-effective automated warehouse systems.