Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling

Abstract

Robots designed to interact socially with people require reliable estimates of human position and motion. Additional pose data such as body orientation may enable a robot to interact more effectively by providing a basis for inferring contextual social information such as people's intentions and relationships. To this end, we have developed a system for simultaneously tracking the position and body orientation of many people, using a network of laser range finders mounted at torso height. An individual particle filter is used to track the position and velocity of each human, and a parametric shape model representing the person's cross-sectional contour is fit to the observed data at each step. We demonstrate the system's tracking accuracy quantitatively in laboratory trials and we present results from a field experiment observing subjects walking through the lobby of a building. The results show that our method can closely track torso and arm movements, even with noisy and incomplete sensor data, and we present examples of social information observable from this orientation and positioning information that may be useful for social robots.

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

“…The size of the observation area is 860 square meters and we observed 12003 pedestrians in 7 hours, of which 85% were used for calibration and 15% for testing. To track the pedestrians we used twenty LRFs (Hokuyo Automatic UTM-30LX) and applied a tracking algorithm based on shape-matching at torso-level [26]. This area is larger than those investigated in previous literature works, and thus the prediction task is harder.…”

Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans

“…To distinguish them from static objects, geometric and statistical features [20], theoretical shape models [21], laser reflection intensity [22] or a threshold [16,17] can be used. The clustering threshold can be chosen empirically as a constant [17] or time-variant value [16].…”

Pedestrian identification by associating wearable and environmental sensors based on phase dependent correlation of human walking

“…Zhao and Shibasaki (2005) also track people by using a simple walking model of pedestrians. Glas et al (2009) placed LRFs in a shopping mall to predict the trajectories of people by observing customers at waist height.…”

“…Our method expands upon the system described in (Glas et al 2009) and uses a particle-filter-based algorithm to track feet in the environment (Fig. 3).…”