Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk-averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source software pipeline and cognitive architecture tool that enables full life cycle AI research for on-board intelligent systems. We begin with a description and user walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to the development and function of each respective scenario. We conclude with remarks on future work, future planned deployments, and goals for the forward progression of OnAIR as a tool to enable a larger AI and aerospace research community.

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.