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Apple’s mobile phone LiDAR capabilities were previously evaluated to obtain geometry from multiple exemplar vehicles, but results were inconsistent and less accurate than traditional ground-based LiDAR (SAE Technical Paper 2022-01-0832. Miller, Hashemian, Gillihan, Helms). This paper builds upon existing research by utilizing the newest version of the mobile LiDAR hardware and software previously studied, as well as evaluating additional objects of varying sizes and a newly released software not yet studied. To better explore the accuracy achievable with Apple mobile phone LiDAR, multiple objects with varied surface textures, colors, and sizes were scanned. These objects included exemplar passenger vehicles (including a motorcycle), a fuel tank, and a spare tire mounted on a chrome wheel. To test the repeatability of the presented methodologies, four participants scanned each object multiple times and created three individual data sets per software.

Ground-based Light Detection and Ranging (LiDAR) using FARO Focus 3D scanners (and other brands of scanners) are repeatedly shown to accurately capture the geometry of accident scenes, accident vehicles, and exemplar vehicles, as well as corresponding evidence from these sources such as roadway gouge marks, vehicle crush depth, debris fields, and burn areas. However, ground-based scanners require expensive and large equipment on-site, as well as other materials that may be required depending on the scenario, such as tripods and alignment spheres. New technologies, such as Apple’s mobile phone LiDAR capture, were released recently for their newer model phones, and these devices offer a way to obtain LiDAR data but with less cumbersome and less expensive equipment. This mobile LiDAR can be captured using many different applications from the App Store which can then be exported into point cloud data.

Forensic disciplines are called upon to locate evidence from a single camera or static video camera, and both the angle of incidence and resolution can limit the accuracy of single image photogrammetry. This research compares a baseline of known 3D data points representing evidence locations to evidence locations determined through single image photogrammetry and evaluates the effect that object resolution (measured in pixels), and angle of incidence has on accuracy. Solutions achieved using an automated process where a camera match alignment is calculated from common points in the 2D imagery and the 3D environment, were compared to solutions achieved in a more manual method by iteratively adjusting the camera’s position, orientation, and field-of-view until an alignment is achieved. This research independently utilizes both methods to achieve photogrammetry solutions and to locate objects within a 3D environment.

Photogrammetry and the accuracy of a photogrammetric solution is reliant on the quality of photographs and the accuracy of pixel location within the photographs. A photograph with lens distortion can create inaccuracies within a photogrammetric solution. Due to the curved nature of a camera’s lens(s), the light coming through the lens and onto the image sensor can have varying degrees of distortion. There are commercially available software titles that rely on a library of known cameras, lenses, and configurations for removing lens distortion. However, to use these software titles the camera manufacturer, model, lens and focal length must be known. This paper presents two methodologies for removing lens distortion when camera and lens specific information is not available. The first methodology uses linear objects within the photograph to determine the amount of lens distortion present. This method will be referred to as the straight-line method.