flatvisp

Flatvisp is a software framework that provides a lightweight approach to visual processing on embedded and edge devices. It centers on a flat, non-hierarchical data model intended to minimize memory footprint and latency while supporting common vision tasks such as feature tracking, pose estimation, and basic 3D reconstruction in constrained environments.

Origin and name: the term flatvisp is a composite of 'flat' and 'visp' (short for visual processing). It is presented as a standalone concept and is not officially linked to any particular existing vision library, though it borrows terminology from established visual processing projects.

Architecture: the framework consists of a minimal core that defines a flat scene representation, a modular pipeline for data processing, and a plugin interface for sensors, processors, and solvers. It aims for portability and deterministic performance, with emphasis on low memory use and predictable timing. The core is typically written in C++ with bindings for higher-level languages.

Applications and adoption: flatvisp is described as suitable for hobbyist robotics, educational kits, and lightweight autonomous agents operating on microcontrollers or single-board computers. While it has not become a widely adopted standard, it is referenced in discussions on compact vision pipelines and resource-constrained vision.

Limitations and future work: the flat data model may limit expressiveness for complex scenes and advanced neural-network inference, and the project faces ongoing development to broaden hardware support and feature completeness.