Generic Distance-Invariant Features for Detecting People with Walking Aid in 2D Laser Range Data

Abstract

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. The proposed approach was used to train classifiers for detecting people without walking aids, people with walkers, people in wheelchairs, and people with crutches. By the use of these features, the detection accuracy of people without walking aids increased from an $F_1$ score of 0.85 to 0.96, compared to the state-of-the-art features of Arras et al. Moreover, people with walkers are detected with an $F_1$ score of 0.95 and people in wheelchairs with an $F_1$ score of 0.94. The proposed detection algorithm takes on average less then 1 % of the resources of a 2.8 GHz CPU core to process 270$^{\circ }$ laser range data with an update rate of 12 Hz.