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.
