Industrial cuVSLAM Benchmark & Integration
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908893254254592 |
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| author | Hana, Charbel Abi Amareen, Kameel Mostafa, Mohamad Slepichev, Dmitry Rabeti, Hesam Wang, Zheng Acharya, Mihir Rizk, Anthony |
| author_facet | Hana, Charbel Abi Amareen, Kameel Mostafa, Mohamad Slepichev, Dmitry Rabeti, Hesam Wang, Zheng Acharya, Mihir Rizk, Anthony |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16240 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Industrial cuVSLAM Benchmark & Integration Hana, Charbel Abi Amareen, Kameel Mostafa, Mohamad Slepichev, Dmitry Rabeti, Hesam Wang, Zheng Acharya, Mihir Rizk, Anthony Robotics 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. |
| title | Industrial cuVSLAM Benchmark & Integration |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.16240 |