cuVSLAM: CUDA accelerated visual odometry and mapping
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918085811765248 |
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| author | Korovko, Alexander Slepichev, Dmitry Efitorov, Alexander Dzhumamuratova, Aigul Kuznetsov, Viktor Rabeti, Hesam Biswas, Joydeep Pouya, Soha |
| author_facet | Korovko, Alexander Slepichev, Dmitry Efitorov, Alexander Dzhumamuratova, Aigul Kuznetsov, Viktor Rabeti, Hesam Biswas, Joydeep Pouya, Soha |
| contents | Accurate and robust pose estimation is a key requirement for any autonomous robot. We present cuVSLAM, a state-of-the-art solution for visual simultaneous localization and mapping, which can operate with a variety of visual-inertial sensor suites, including multiple RGB and depth cameras, and inertial measurement units. cuVSLAM supports operation with as few as one RGB camera to as many as 32 cameras, in arbitrary geometric configurations, thus supporting a wide range of robotic setups. cuVSLAM is specifically optimized using CUDA to deploy in real-time applications with minimal computational overhead on edge-computing devices such as the NVIDIA Jetson. We present the design and implementation of cuVSLAM, example use cases, and empirical results on several state-of-the-art benchmarks demonstrating the best-in-class performance of cuVSLAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | cuVSLAM: CUDA accelerated visual odometry and mapping Korovko, Alexander Slepichev, Dmitry Efitorov, Alexander Dzhumamuratova, Aigul Kuznetsov, Viktor Rabeti, Hesam Biswas, Joydeep Pouya, Soha Robotics Artificial Intelligence Software Engineering Accurate and robust pose estimation is a key requirement for any autonomous robot. We present cuVSLAM, a state-of-the-art solution for visual simultaneous localization and mapping, which can operate with a variety of visual-inertial sensor suites, including multiple RGB and depth cameras, and inertial measurement units. cuVSLAM supports operation with as few as one RGB camera to as many as 32 cameras, in arbitrary geometric configurations, thus supporting a wide range of robotic setups. cuVSLAM is specifically optimized using CUDA to deploy in real-time applications with minimal computational overhead on edge-computing devices such as the NVIDIA Jetson. We present the design and implementation of cuVSLAM, example use cases, and empirical results on several state-of-the-art benchmarks demonstrating the best-in-class performance of cuVSLAM. |
| title | cuVSLAM: CUDA accelerated visual odometry and mapping |
| topic | Robotics Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2506.04359 |