People detection and distinction of their walking aids in 2D laser range data based on generic distance-invariant features

People detection in 2D laser range data is a popular cue for person tracking in mobile robotics. Many approaches are designed to detect pairs of legs. These approaches perform well in many public environments. However, we are working on an assistance robot for stroke patients in a rehabilitation center, where most of the people need walking aids. These tools occlude or touch the legs of the patients. Thereby, approaches based on pure leg detection fail. The essential contribution of this paper are generic distance-invariant range scan features for people detection in 2D laser range data and the distinction of their walking aids. With these features we trained classifiers for detecting people without walking aids (or with crutches), people with walkers, and people in wheelchairs. Using this approach for people detection, we achieve an F₁ score of 0.99 for people with and without walking aids, and 86% of detections are classified correctly regarding their walking aid. For comparison, using state-of-the-art features of Arras et al. on the same data results in an F₁ score of 0.86 and 57% correct discrimination of walking aids. The proposed detection algorithm takes around 2.5% of the resources of a 2.8 GHz CPU core to process 270° laser range data at an update rate of 10 Hz.