This paper presents a field-deployable autonomous 3D scanning framework capable of self-decision-making in unknown, cluttered outdoor environments. The proposed system introduces a two-level decision logic that enables a robot to independently navigate, select scan viewpoints, and iteratively update 3D maps under field constraints. Frontier-based exploration and visibility-based scan-stop evaluation are integrated into a unified workflow optimized for operational transparency, safety, and computational efficiency. This study contributes a validated framework and practical deployment strategy that enable autonomous data collection in safety-critical, GNSS-denied environments. The framework was implemented and tested both in simulation and at a disaster site, demonstrating consistent navigation, reliable viewpoint selection, and high-resolution mapping of reachable areas. The findings highlight design principles, decision logic, and integration practices that enable robust, repeatable operation for autonomous 3D scanning in construction and disaster-response applications.
Kim et al. (Wed,) studied this question.