Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for Vineyards

Hroob, Ibrahim; Polvara, Riccardo; Mellado, Sergio Molina; Cielniak, Grzegorz; Hanheide, Marc

doi:10.1007/978-3-030-89177-0_17

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…It includes RGB images, fruit labels, the camera position from which the images were taken, and pointers to other images that can be accessed by performing an action. Hroob et al (2021) create a simulated vineyard environment and test SLAM algorithms in those environments.…”

Section: Related Workmentioning

confidence: 99%

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Marzoa Tanco,

Trinidad Barnech,

Andrade

et al. 2023

The International Journal of Robotics Research

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The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.

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Section: Related Workmentioning

confidence: 99%

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Marzoa Tanco,

Trinidad Barnech,

Andrade

et al. 2023

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…The Real-Time Kinematic Global Positioning System (RTK-GPS) is a widely used localization technique in field robotics, where high precision is required. RTK-GPS can provide localization accuracy of up to a few centimeters by using a combination of satellite-based positioning and ground-based reference stations [2]. However, RTK-GPS systems come with certain limitations such as high cost, reliance on subscriptions and external infrastructure, susceptibility to environmental factors and weather conditions, which can lead to degraded performance and reliability.…”

Section: Introductionmentioning

confidence: 99%

Learned Long-Term Stability Scan Filtering for Robust Robot Localisation in Continuously Changing Environments

Hroob,

Molina,

Polvara

et al. 2023

2023 European Conference on Mobile Robots (ECMR)

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In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot's position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for longterm missions compared to using the entire LiDAR frame points.

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Bacchus Long‐Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment

Polvara

Molina

Hroob

et al. 2023

Journal of Field Robotics

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Achieving a robust long‐term deployment with mobile robots in the agriculture domain is both a demanded and challenging task. The possibility to have autonomous platforms in the field performing repetitive tasks, such as monitoring or harvesting crops, collides with the difficulties posed by the always‐changing appearance of the environment due to seasonality. With this scope in mind, we report an ongoing effort in the long‐term deployment of an autonomous mobile robot in a vineyard, with the main objective of acquiring what we called the Bacchus Long‐Term (BLT) data set. This data set consists of multiple sessions recorded in the same area of a vineyard but at different points in time, covering a total of 7 months to capture the whole canopy growth from March until September. The multimodal data set recorded is acquired with the main focus put on pushing the development and evaluations of different mapping and localization algorithms for long‐term autonomous robots operation in the agricultural domain. Hence, besides the data set, we also present an initial study in long‐term localization using four different sessions belonging to four different months with different plant stages. We identify that state‐of‐the‐art localization methods can only cope partially with the amount of change in the environment, making the proposed data set suitable to establish a benchmark on which the robotics community can test its methods. On our side, we anticipate two solutions pointed at extracting stable temporal features for improving long‐term 4D localization results. The BLT data set is available at https://lncn.ac/lcas-blt.

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Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for Vineyards

Cited by 7 publications

References 17 publications

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Learned Long-Term Stability Scan Filtering for Robust Robot Localisation in Continuously Changing Environments

Bacchus Long‐Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment

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