# FROG: a new people detection dataset for knee-high 2D range finders

## TL;DR

The paper introduces FROG, a new dataset for detecting people using knee-high 2D range finders on robots, offering better resolution and annotations than existing datasets.

## Contribution

FROG provides a more comprehensive and annotated dataset for people detection using 2D range finders, along with a new deep learning approach that works directly with raw sensor data.

## Key findings

- The FROG dataset has 100% annotated laser scans, 17x more annotated scans, and 100x more people annotations than DROW.
- A new end-to-end deep learning approach achieves state-of-the-art results without requiring hand-crafted features or preprocessing.
- An optimized ROS implementation of the proposed detector operates at over 500 Hz.

## Abstract

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.