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CuVSLAM: CUDA accelerated visual odometry and mapping

Created by
  • Haebom

Author

Alexander Korovko, Dmitry Slepichev, Alexander Efitorov, Aigul Dzhumamuratova, Viktor Kuznetsov, Hesam Rabeti, Joydeep Biswas, Soha Pouya

Outline

CuVSLAM is a state-of-the-art Visual Simultaneous Localization and Mapping (VSLAM) solution that leverages a variety of visual-inertial sensor combinations, including multiple RGB and depth cameras and inertial measurement units. It supports from one to up to 32 RGB cameras in arbitrary geometric configurations, making it applicable to a variety of robotic setups. It is optimized using CUDA for deployment in real-time applications with minimal computational overhead on edge computing devices such as NVIDIA Jetson. In this paper, we present the design and implementation of cuVSLAM, use cases, and experimental results on state-of-the-art benchmarks, demonstrating state-of-the-art performance.

Takeaways, Limitations

Takeaways:
Providing a flexible VSLAM system supporting various visual-inertial sensor combinations.
Efficient computation through CUDA optimization for real-time operation on edge computing devices.
Demonstrated top performance in multiple benchmarks.
Expanding applicability to various robot platforms.
Limitations:
Specific Limitations is not mentioned in this paper. Additional experiments and analyses are needed to identify performance limitations in various environments and conditions.
Additional information is needed regarding dependencies on specific sensors or environments.
Lack of information on source code availability and extensibility.
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