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A modular, extensible framework for modern visual SLAM systems

Visual SLAM is a long-standing research area with many significant advances over the years. New systems typically build on previous contributions, but this requires significant development overhead, a highly detailed understanding of previous system implementations, and is rife with programming pitfalls. To enable fast experimentation and reduce the need for researchers to re-invent the wheel, we propose an extensible Visual SLAM framework with three features: modularity, seamless edge offloading, and safe concurrency.