Benchmark Dataset for Evaluation of Range-Based People Tracker Classifiers in Mobile Robots

Abstract

[EN] Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we present a new dataset.Data actually contained in the dataset allow evaluating two people trackers, both neural network-based: leg detector (LD), a widely used solution by the Robot Operating System (ROS) community; and a people-tracker tool developed by the Robotics Group at the University of Leon, known as PeTra.