  This paper introduces the open-source framework, GIRA, which implements
fundamental robotics algorithms for reconstruction, pose estimation, and
occupancy modeling using compact generative models. Compactness enables
perception in the large by ensuring that the perceptual models can be
communicated through low-bandwidth channels during large-scale mobile robot
deployments. The generative property enables perception in the small by
providing high-resolution reconstruction capability. These properties address
perception needs for diverse robotic applications, including multi-robot
exploration and dexterous manipulation. State-of-the-art perception systems
construct perceptual models via multiple disparate pipelines that reuse the
same underlying sensor data, which leads to increased computation, redundancy,
and complexity. GIRA bridges this gap by providing a unified perceptual
modeling framework using Gaussian mixture models (GMMs) as well as a novel
systems contribution, which consists of GPU-accelerated functions to learn GMMs
10-100x faster compared to existing CPU implementations. Because few GMM-based
frameworks are open-sourced, this work seeks to accelerate innovation and
broaden adoption of these techniques.
