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cuVSLAM: CUDA accelerated visual odometry and mapping

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arxiv 2506.04359 v3 pith:H4P32XUV submitted 2025-06-04 cs.RO cs.AIcs.SE

Alexander Korovko , Dmitry Slepichev , Alexander Efitorov , Aigul Dzhumamuratova , Viktor Kuznetsov , Hesam Rabeti , Joydeep Biswas , Soha Pouya This is my paper
classification cs.ROcs.AIcs.SE
keywords cuvslamcamerascudamappingstate-of-the-artvisualacceleratedaccurate
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM

    cs.RO 2026-07 conditional novelty 5.0 of 10

    GLidE-SLAM moves pose-only photometric tracking to OpenGL ES compute shaders, reporting up to 9x faster frame rates than ORB-SLAM2 on embedded platforms with comparable ATE on TUM and EuRoC sequences.

  2. CuSfM: CUDA-Accelerated Structure-from-Motion

    cs.CV 2025-10 conditional novelty 5.0 of 10

    A CUDA-accelerated SfM system reports faster offline mapping than COLMAP and better trajectory accuracy when initialized with odometry-like poses, though the comparison is not fully fair.

  3. BIM Informed Visual SLAM for Construction Environments

    cs.RO 2025-09 conditional novelty 5.0 of 10

    Adding BIM wall correspondences as fixed-node constraints in a visual SLAM back-end reduces average ATE by 23.71% and map RMSE by 7.14% on the authors' collected construction and office sequences.

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