Assessing iPhone LiDAR & Recon‐3D for determining area of origin in bloodstain pattern analysis

Abstract

AbstractBloodstain pattern analysis (BPA) has proven to be a useful tool in forensic and criminal investigations for quite some time. Traditionally, documenting a crime scene for a bloodletting event was completed using manual techniques, physical strings, and a tape measure. In more recent years, laser scanners and 3D software programs have become a preferred method to capture accurate data that improves the validity and reliability of BPA. The initial cost of laser scanning equipment is relatively high, rendering these systems inaccessible to some police and smaller agencies. Recon‐3D is a newly developed iPhone application that utilizes the iPhone LiDAR sensor in combination with video data to create 3D point clouds of crime scenes. To assess the viability of Recon‐3D for area of origin analysis, two tests were performed. One was a series of bloodstain impacts which were analyzed in FARO Zone 3D software, while the second was a series of 6 repeated Recon‐3D scans of two 90‐degree walls which was then compared to the FARO Focus S350 scanner using CloudCompare software. A total of eight impact patterns were made at three different distances from a wall. The area of origin was measured and compared to the known location of the blood source. The average total 3D error for the area of origin set at 25, 50, and 100 cm from two perpendicular walls was found to be 6.04, 15.16, and 36.59 cm, respectively. These results are similar to past studies where programs such as HemoSpat have been used. The results of the point cloud comparison show that on average, 95% of the points from Recon‐3D fall below a threshold of 3.6 mm when compared to a FARO Focus S350 laser scanner. Thus, the results of this test suggest that Recon‐3D is an accurate and affordable scanning application for bloodstain patterns at crime scenes and the data provide acceptable results for area of origin analysis in BPA programs which accept laser scanner data.