Remote sensing-based model generation for geoengineering applications requires a robust computational framework to quantify uncertainty and evaluate thresholds of measurable intervals. The combination of partially mutually-inclusive scans induces inherent and quantifiable uncertainty for engineering measurements. Iterative Closest Point (ICP) registration with recursive weighing of excessively far-off datapoints presents an acknowledged solution for scan placement. The resulting quality of conventional ICP is severely impacted by localized mutual-exclusions (i.e. relative occlusions). The present work proposes a recursive ICP (R-ICP) followed by systematic excessive bias filtering loops to enhance the quality of scan registration, while offering an intuitive and explicit perspective on quantifiable placement quality. The method is exemplified with a series of examples, followed by validation schemes to quantify the limitations of the proposed method.