RT Journal Article
T1 FROG: a new people detection dataset for knee-high 2D range finders
A1 Amodeo Zurbano, Fernando
A1 Pérez Higueras, Noé
A1 Merino, Luis
A1 Caballero, Fernando
K1 Human-aware robotics
K1 2D LIDAR
K1 People detection
K1 Dataset
K1 ROS
K1 Benchmark
K1 Deep learning
AB Mobile robots require knowledge of the environment, especially of humans located in its vicinity. While the most common approaches for detecting humans involve computer vision, an often overlooked hardware feature of robots for people detection are their 2D range finders. These were originally intended for obstacle avoidance and mapping/SLAM tasks. In most robots, they are conveniently located at a height approximately between the ankle and the knee, so they can be used for detecting people too, and with a larger field of view and depth resolution compared to cameras. In this paper, we present a new dataset for people detection using knee-high 2D range finders called FROG. This dataset has greater laser resolution, scanning frequency, and more complete annotation data compared to existing datasets such as DROW (Beyer et al., 2018). Particularly, the FROG dataset contains annotations for 100% of its laser scans (unlike DROW which only annotates 5%), 17x more annotated scans, 100x more people annotations, and over twice the distance traveled by the robot. We propose a benchmark based on the FROG dataset, and analyze a collection of state-of-the-art people detectors based on 2D range finder data. We also propose and evaluate a new end-to-end deep learning approach for people detection. Our solution works with the raw sensor data directly (not needing hand-crafted input data features), thus avoiding CPU preprocessing and releasing the developer of understanding specific domain heuristics. Experimental results show how the proposed people detector attains results comparable to the state of the art, while an optimized implementation for ROS can operate at more than 500 Hz.
PB Frontiers
YR 2025
FD 2025-10-20
LK https://hdl.handle.net/10433/24913
UL https://hdl.handle.net/10433/24913
LA en
NO Frontiers in Robotics and AI, Volume 12 - 2025
NO The author(s) declare that financial support was received for the research and/or publication of this article. FA is supported by the predoctoral grant PRE2022-105119 as part of the INSERTION project (PID2021-127648OB-C31), funded by Ministerio de Ciencia e Innovación. This work is partially supported by the project PICRAH4.0 (PLEC2023-010353) funded by programa Transmisiones 2023 del Ministerio de Ciencia e Innovación, and by the project NORDIC (TED2021-132476B-I00) funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/“PRTR”.
NO Proyectos de Investigación:TED2021-132476B-I00
NO Service Robotics Lab
NO Universidad Pablo de Olavide
DS RIO
RD May 9, 2026