Topological Data Analysis for Navigation in Unstructured Environments 2023-01-0088

Autonomous vehicle navigation, both global and local, makes use of large amounts of multifactorial data from onboard sensors, prior information, and simulations to safely navigate a chosen terrain. Additionally, as each mission has a unique set of requirements, operational environment and vehicle capabilities, any fixed formulation for the cost associated with these attributes is sub-optimal across different missions. Much work has been done in the literature on finding the optimal cost definition and subsequent mission pathing given sufficient measurements of the preference over the mission factors. However, obtaining these measurements can be an arduous and computationally expensive task. Furthermore, the algorithms that utilize this large amount of multifactorial data themselves are time consuming and expensive. Often, it is valuable to make assessments about a terrain with limited information and using similarity with existing terrains without necessarily performing the entire simulation. This paper will investigate how topological data analysis (TDA) can be used to describe ontological features of the collected terrain data and how those features can be used to help navigation of the mission without making assumptions of the mission requirements or operator preferences.