The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments

TL;DR

The Rosario Dataset provides realistic multisensor data from agricultural environments to facilitate research in SLAM, odometry, and sensor fusion for autonomous farming robots.

Contribution

It introduces a new, publicly available dataset with synchronized multisensor recordings in challenging agricultural scenes for benchmarking SLAM and related tasks.

Findings

Dataset includes 6 challenging soybean field sequences.

Contains synchronized wheel odometry, IMU, stereo camera, and GPS-RTK data.

Aims to advance agricultural robotics research.

Abstract

In this paper we present The Rosario Dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of 6 sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural SLAM/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, IMU, stereo camera and a GPS-RTK system. The dataset is publicly available in http://www.cifasis-conicet.gov.ar/robot/.