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 six 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 simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/.
Alencastre-MirandaMDavidsonJRJohnsonRMWaguespackHKrebsHI (2018) Robotics for sugarcane cultivation: Analysis of billet quality using computer vision. IEEE Robotics and Automation Letters3(4): 3828–3835.
2.
AllanDW (1966) Statistics of atomic frequency standards. Proceedings of the IEEE54(2): 221–230.
3.
Blanco-ClaracoJLMoreno-DueñasFÁGonzález-JiménezJ (2014) The Málaga urban dataset: High-rate stereo and LIDAR in a realistic urban scenario. The International Journal of Robotics Research33(2): 207–214.
4.
BurriMNikolicJGohlPet al. (2016) The EuRoC micro aerial vehicle datasets. The International Journal of Robotics Research35(10): 1157–1163.
5.
Carlevaris-BiancoNUshaniAKEusticeRM (2016) University of Michigan North Campus long-term vision and LIDAR dataset. The International Journal of Robotics Research35(9): 1023–1035.
6.
ChebroluNLottesPSchaeferAWinterhalterWBurgardWStachnissC (2017) Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. The International Journal of Robotics Research36(10): 1045–1052.
7.
Di CiccoMPotenaCGrisettiGPrettoA (2017) Automatic model based dataset generation for fast and accurate crop and weeds detection. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
8.
DiasPATabbAMedeirosH (2018) Multispecies fruit flower detection using a refined semantic segmentation network. IEEE Robotics and Automation Letters3(4): 3003–3010.
9.
FentanesJPGouldIDuckettTPearsonSCielniakG (2018) 3D soil compaction mapping through kriging-based exploration with a mobile robot. arXiv preprint arXiv:1803.08069.
10.
FurgalePBarfootTDSibleyG (2012a) Continuous-time batch estimation using temporal basis functions. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 2088–2095.
11.
FurgalePCarlePEnrightJBarfootTD (2012b) The Devon Island rover navigation dataset. The International Journal of Robotics Research31(6): 707–713.
12.
FurgalePRehderJSiegwartR (2013) Unified temporal and spatial calibration for multi-sensor systems. In: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1280–1286.
13.
GeigerALenzPStillerCUrtasunR (2013) Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research32(11): 1231–1237.
14.
GriffithSChahineGPradalierC (2017) Symphony Lake dataset. The International Journal of Robotics Research36(11): 1151–1158.
15.
HaugSOstermannJ (2014) A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In: Proceedings of the European Conference on Computer Vision (ECCV). Berlin: Springer, pp. 105–116.
16.
LeungKLührDHoushiarHet al. (2017) Chilean underground mine dataset. The International Journal of Robotics Research36(1): 16–23.
17.
MaddernWPascoeGLinegarCNewmanP (2017) 1 year, 1000 km: The Oxford RobotCar dataset. The International Journal of Robotics Research36(1): 3–15.
18.
MajdikALTillCScaramuzzaD (2017) The Zurich urban micro aerial vehicle dataset. The International Journal of Robotics Research36(3): 269–273.
19.
MayeJFurgalePSiegwartR (2013) Self-supervised calibration for robotic systems. In: Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, pp. 473–480.
20.
MillerMChungSJHutchinsonS (2018) The visual–inertial canoe dataset. The International Journal of Robotics Research37(1): 13–20.
21.
Mur-ArtalRTardósJD (2017) ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics33(5): 1255–1262.
22.
OlsonE (2011) AprilTag: A robust and flexible visual fiducial system. In: Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3400–3407.
23.
PandeyGMcBrideJREusticeRM (2011) Ford Campus vision and LIDAR data set. International Journal of Robotics Research30(13): 1543–1552.
24.
PezzementiZTaborTHuPet al. (2018) Comparing apples and oranges: Off-road pedestrian detection on the national robotics engineering center agricultural person-detection dataset. Journal of Field Robotics35(4): 545–563.
25.
PireTFischerTCastroGDe CristóforisPCiveraJJacobo BerllesJ (2017) S-PTAM: Stereo parallel tracking and mapping. Robotics and Autonomous Systems93: 27–42.
26.
PireTFischerTCiveraJDe CristóforisPBerllesJJ (2015) Stereo parallel tracking and mapping for robot localization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1373–1378.
27.
PistarelliMPireTKofmanE (2017) Caracterización de un sistema GPS RTK de bajo costo. In: Actas de las IX Jornadas Argentinas de Robótica. Córdoba, Argentina: Facultad Regional Córdoba de la Universidad Tecnológica Nacional, pp. 11–16.
28.
Ruiz-SarmientoJGalindoCGonzález-JiménezJ (2017) Robot@home, a robotic dataset for semantic mapping of home environments. The International Journal of Robotics Research36(2): 131–141.
29.
SaIChenZPopovićMet al. (2018) weedNet: Dense semantic weed classification using multispectral images and MAV for smart farming. IEEE Robotics and Automation Letters3(1): 588–595.
30.
SmithMBaldwinIChurchillWPaulRNewmanP (2009) The New College vision and laser data set. The International Journal of Robotics Research28(5): 595–599.
31.
SturmJEngelhardNEndresFBurgardWCremersD (2012) A benchmark for the evaluation of RGB-D SLAM systems. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 573–580.
32.
TongCHGingrasDLaroseKBarfootTDDupuisÉ (2013) The Canadian planetary emulation terrain 3D mapping dataset. The International Journal of Robotics Research32(4): 389–395.
33.
WeinsteinAJMooreKL (2010) Pose estimation of Ackerman steering vehicles for outdoors autonomous navigation. In: Proceedings of the 2010 IEEE International Conference on Industrial Technology, pp. 579–584.
