Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment.