Nano Drone-based Indoor Crime Scene Analysis
Martin Cooney, Sivadinesh Ponrajan, Fernando Alonso-Fernandez
TL;DR
This paper tackles the problem of safely and efficiently analyzing indoor crime scenes using nano drones. It adopts a speculative prototyping approach guided by the STAIR framework to identify unaddressed CSA tasks and then demonstrates three capabilities on a low-cost drone: entry via partially opened windows, mapping and collecting evidence, and initial blood smear direction inference. The authors report preliminary lab results with performance levels of 75%, 85%, and 80% for the three capabilities and discuss practical challenges such as low thrust, lighting, and evidence disturbance, offering a roadmap for more capable future systems. The work provides theoretical gaps and pragmatic prototypes that could accelerate the development of autonomous, privacy-conscious CSA tools while highlighting safety, security, and ethical considerations for real-world deployment.
Abstract
Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculative prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime scenes through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work.