Abstract
Robust mapping capabilities are a critical technology for intelligent robotic systems. They can (1) provide valuable information to human and robot teammates without requiring prior knowledge or experience and (2) enable other, higher-level behaviors, such as autonomous navigation and exploration. To maximize interpretability, a map must be coherent, accurate, and displayed in an intuitive fashion. However, maps inherently require a large amount of computational resources. Therefore, it is beneficial to determine the minimum amount of information that must be provided to a user to meet the specific mission requirements. The purpose of this study is to evaluate human performance on visual tasks using 2D and 3D maps generated from laser point cloud data. In a within-subjects study, 20 participants were tasked with locating and identifying objects, doorways, and windows in a two-story building. The characterizations made herein could ultimately influence how map data from robotic assets are shared and displayed.