This work presents a comprehensive benchmark evaluation of visual odometry (VO) and visual SLAM (VSLAM) systems for mobile robot navigation in real-world logistical environments. We compare multiple visual odometry approaches across controlled trajectories covering translational, rotational, and mixed motion patterns, as well as a large-scale production facility dataset spanning approximately 1.7 km. Performance is evaluated using Absolute Pose Error (APE) against ground truth from a Vicon motion capture system and a LiDAR-based SLAM reference. Our results show that a hybrid stack combining the cuVSLAM front-end with a custom SLAM back-end achieves the strongest mapping accuracy, motivating a deeper integration of cuVSLAM as the core VO component in our robotics stack. We further validate this integration by deploying and testing the cuVSLAM-based VO stack on an NVIDIA Jetson platform.
@article{arxiv.2603.16240,
title = {Industrial cuVSLAM Benchmark & Integration},
author = {Charbel Abi Hana and Kameel Amareen and Mohamad Mostafa and Dmitry Slepichev and Hesam Rabeti and Zheng Wang and Mihir Acharya and Anthony Rizk},
journal= {arXiv preprint arXiv:2603.16240},
year = {2026}
}