LAUSR

We propose a visualization system for incident commanders (ICs) in urban search and rescue scenarios that supports path planning in post‐disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for the assessment of automatically generated paths. As data uncertainty and a priori unknown information make fully automated systems impractical, we present the IC with a set of viable access paths, based on varying risk factors, in a 3D environment combined with visual analysis tools enabling informed decision making and trade‐offs. Based on these decisions, a responder is guided along the path by the IC, who can interactively annotate and reevaluate the acquired point cloud and generated paths to react to the dynamics of the situation. We describe visualization design considerations for our system and decision support systems in general, technical realizations of the visualization components, and discuss the results of two qualitative expert evaluation; one online study with nine search and rescue experts and an eye‐tracking study in which four experts used the system on an application case.