Evaluation of Virtual Measurements of Scanned Dental Impressions

Introduction: Digital models have been proposed as substitutes for plaster models. Dental arch impression scanning is a rapid and practical approach for digital model obtainment; however, few studies have accessed the accuracy of the method. This study verified the accuracy of virtual measurements obtained with the scanner Ortho Insight 3D, version 5.0 (Motionview Software, LLC, Chattanooga, Tennessee, USA). Materials And Methods: Total of 26 plaster models belonging to the Bahia Federal University Orthodontic postgraduation program were divided into three groups: G1 (plaster models); G2 (alginate impressions scan) and G3 (plaster models scan). Virtual measurements were compared to the manual by evaluating upper intercanine and intermolar distances and the antero posterior distance between upper left canine and upper left molar. Paired Student t and Lin agreement (5% of significance level) were employed for the statistical analysis. Results: Of the evaluated measures, the mean difference ranged from 0.48mm to 0.55mm when compared G2 and G1. The mean difference ranged from 0.6mm to 0.65mm between G3 and G1 groups. Comparing the groups G2 and G3 to G1, it was found statistically significant differences between all variables (p 0.99) for all parameters evaluated. Conclusion: Despite having existed significant differences between the virtual and physical approaches, substantial and almost perfect agreement between them allow us to suggest that there is accuracy of virtual images obtained from scanned impressions in alginate using the laser scanner Ortho Insight 3D

Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks