A laser-based people tracker

Abstract

Describes a method for real-time tracking of people in everyday environments, using multiple planar laser range-finders. People tracking is a well-studied problem in machine vision; we adapt some of those methods to laser range-finders. We group range measurements into entities such as blobs and objects, and use a Kalman filter to estimate trajectories for these objects. The filter is able to generate smooth trajectories, even when objects are occluded. The paper presents our evaluation of the tracker's performance in a series of four experiments.