Chase Cassandra E, Liscio Eugene
Forensic Science Department, Trent University, 1600 W Bank Drive, Peterborough, Ontario K9L 0G2, Canada.
ai2-3D Forensics, 271 Jevlan Drive, Woodbridge, Ontario L4L 8A4, Canada.
Forensic Sci Int. 2023 Sep;350:111787. doi: 10.1016/j.forsciint.2023.111787. Epub 2023 Jul 18.
Bullet trajectory documentation is an important part of shooting reconstructions. Manual methods are quite common, using a protractor and angle gauge, but more advanced methods using laser scanners and photogrammetry are also used for documentation. Past studies have shown that using terrestrial laser scanners (TLS) can reduce the variability in trajectory documentation. In 2020, Apple Inc. released the iPad Pro & iPhone 12 Pro with a Light Detection and Ranging (LiDAR) sensor, which effectively placed a low-cost laser scanner in mobile devices. In May of 2022, an iOS application focused on forensics called Recon-3D was released. Recon-3D uses Apple's LiDAR sensor and fuses it with photogrammetry to produce 3D point cloud data in the e57 format. The purpose of this research was to compare the accuracy, repeatability, and quality of 3D data, using Recon-3D to the Faro Focus S350 laser scanner in bullet trajectory documentation. As a first test, twelve trajectory rods were installed on a wooden panel in a controlled, indoor environment, in order to optimize the best settings for scanning trajectory rods. Subsequently, a more practical exercise was run outdoors with a vehicle containing five trajectory rods. The mean horizontal angle error for the twelve trajectory rods installed on the wooden panel was 0.14° with a standard deviation of 0.30°. The mean error for the vertical angle was - 0.05° with a standard deviation of 0.25°. For the outdoor vehicle test, the mean error for the horizontal angle was 0.03° with a standard deviation of 0.28°. The mean error for the vertical angle was 0.22° with a standard deviation of 0.36°. These errors are similar to other studies which utilize the terrestrial laser scanner with mean errors well below 1°. Although further work is required to make Recon-3D a more robust application, preliminary results are promising and in line with previous studies where the terrestrial laser scanner has been used. Thus, Recon-3D appears suitable to document trajectory rods when used in shooting reconstructions.
子弹轨迹记录是枪击重建的重要组成部分。手动方法很常见,使用量角器和角度规,但也使用更先进的方法,如激光扫描仪和摄影测量法进行记录。过去的研究表明,使用地面激光扫描仪(TLS)可以减少轨迹记录中的变异性。2020年,苹果公司发布了配备光探测和测距(LiDAR)传感器的iPad Pro和iPhone 12 Pro,这有效地将低成本激光扫描仪集成到了移动设备中。2022年5月,一款专注于法医学的iOS应用程序Recon-3D发布。Recon-3D使用苹果的LiDAR传感器,并将其与摄影测量法相结合,以生成e57格式的三维点云数据。本研究的目的是在子弹轨迹记录中,比较使用Recon-3D和Faro Focus S350激光扫描仪生成的三维数据的准确性、可重复性和质量。作为第一次测试,在可控的室内环境中的一块木板上安装了12根轨迹杆,以优化扫描轨迹杆的最佳设置。随后,在户外对一辆装有5根轨迹杆的车辆进行了更实际的测试。安装在木板上的12根轨迹杆的平均水平角误差为0.14°,标准差为0.30°。垂直角的平均误差为-0.05°,标准差为0.25°。对于户外车辆测试,水平角的平均误差为0.03°,标准差为0.28°。垂直角的平均误差为0.22°,标准差为0.36°。这些误差与其他使用地面激光扫描仪的研究相似,平均误差远低于1°。尽管需要进一步开展工作,以使Recon-3D成为更强大的应用程序,但初步结果很有前景,并且与之前使用地面激光扫描仪的研究结果一致。因此,在枪击重建中使用时,Recon-3D似乎适用于记录轨迹杆。