Domestic robots developed to support human beings by performing daily tasks such as cleaning should also be able to help in emergencies by finding, analysing, and assisting persons in need of first aid. Here such a robot capable of performing some useful task related to first aid is referred to as a First Aid Mobile Robot (FAMR). One challenge which to the author's knowledge has not been solved is how such a FAMR can find a fallen person's pose within an environment, recognising locations of points of interest for first aid such as the mouth, nose, chin, chest and hands on a map. To overcome the challenge, a new approach is introduced based on leveraging a robot's capabilities (multiple sensors and mobility), called AHBL. AHBL comprises four steps: Anomaly detection, Human detection, Body part recognition, and Localisation on a map. It was broken down into four steps for modularity (e.g., a different way of detecting anomalies can be slipped in without changing the other modules) and because it was not clear which step is hardest to implement. As a result of evaluating AHBL, a FAMR developed for this work was able to find the pose of a fallen person (a mannequin) in a known environment with an average success rate of 83%, and an average localisation discrepancy of 1.47cm between estimated body part locations and ground truth. The presented approach can be adapted for use in other robots and contexts, and can act as a starting point toward designing systems for autonomous robotic first aid.