cuVSLAM: CUDA accelerated visual odometry and mapping

• URL: http://arxiv.org/abs/2506.04359v3
• Date: Tue, 08 Jul 2025 16:53:53 GMT
• Title: cuVSLAM: CUDA accelerated visual odometry and mapping
• Authors: Alexander Korovko, Dmitry Slepichev, Alexander Efitorov, Aigul Dzhumamuratova, Viktor Kuznetsov, Hesam Rabeti, Joydeep Biswas, Soha Pouya,
• Abstract summary: cuVSLAM is a state-of-the-art solution for visual simultaneous localization and mapping.<n>It can operate with a variety of visual-inertial sensor suites, including multiple RGB and depth cameras, and inertial measurement units.
• Score: 72.43057259584663
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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