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arXiv · 2603.16240

Industrial cuVSLAM Benchmark & Integration

Charbel Abi Hana·Kameel Amareen·Mohamad Mostafa·Dmitry Slepichev·Hesam Rabeti·Zheng Wang·Mihir Acharya·Anthony Rizk
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Abstract

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.

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Charbel Abi Hana, Kameel Amareen, Mohamad Mostafa, Dmitry Slepichev, Hesam Rabeti, Zheng Wang, Mihir Acharya, Anthony Rizk. 2026-03-17. Industrial cuVSLAM Benchmark & Integration. https://arxiv.org/abs/2603.16240

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