ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Industrial Robotics and Automation
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1541017
This article is part of the Research TopicInnovations in Industry 4.0: Advancing Mobility and Manipulation in RoboticsView all 5 articles
VIO-GO: Optimizing Event-Based SLAM Parameters for Robust Performance in High Dynamic Range Scenarios
Provisionally accepted
- 1Rochester Institute of Technology Dubai, Dubai, United Arab Emirates
- 2University of Sharjah, Sharjah, United Arab Emirates
- 3School of Engineering, Lebanese American University, Byblos, Lebanon
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This paper addresses a critical challenge in Industry 4.0 robotics by enhancing Visual Inertial Odometry (VIO) systems to operate effectively in dynamic and low-light industrial environments, which are common in sectors like warehousing, logistics, and manufacturing. Inspired by biological sensing mechanisms, we integrate bio-inspired event cameras to improve state estimation systems performance in both dynamic and low-light conditions, enabling reliable localization and mapping. The proposed state estimation framework integrates events, conventional video frames, and inertial data to achieve reliable and precise localization with specific emphasis on real-world challenges posed by high-speed and cluttered settings typical in Industry 4.0.Despite advancements in event-based sensing, there is a noteworthy gap in optimizing Event Simultaneous Localization and Mapping (SLAM) parameters for practical applications. To address this, we introduce a novel VIO-Gradient-based Optimization (VIO-GO) method that employs Batch Gradient Descent (BGD) for efficient parameter tuning. This automated approach determines optimal parameters for Event SLAM algorithms by using motion-compensated images to represent event data. Experimental validation on the Event Camera Dataset shows a remarkable 60% improvement in Mean Position Error (MPE) over fixed-parameter methods. Our results demonstrate that VIO-GO consistently identifies optimal parameters, enabling precise VIO performance in complex, dynamic scenarios essential for Industry 4.0 applications. Additionally, as parameter complexity scales, VIO-GO achieves a 24% reduction in MPE when using the most comprehensive parameter set (VIO-GO8) compared to a minimal set (VIO-GO2), highlighting the method's scalability and robustness for adaptive robotic systems in challenging industrial environments.
Keywords: Visual Inertial Odometry, Event SLAM, Batch gradient descent, optimization, Edge image, Dynamic and low-light environments
Received: 06 Dec 2024; Accepted: 01 Jul 2025.
Copyright: © 2025 Mounsef, Sakhrieh, Singh, Arain and Maalouf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jinane Mounsef, Rochester Institute of Technology Dubai, Dubai, United Arab Emirates
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