Automated Topological Mapping for Agricultural Robots

Heiwolt, Karoline; Mandil, Willow; Cielniak, Grzegorz; Hanheide, Marc

doi:10.31256/ze8ex1v

Cited by 2 publications

(1 citation statement)

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“…The computation of the topological map can be either done before the robot operation, or in an online manner, that is, during the SLAM operation. For example, Heiwolt et al (2020) extract the topological map from high‐resolution satellite images. This means that the map is prepartitioned before the robot operation, which has two main advantages: there is no need to spend time and resources computing the map nodes and edges during the SLAM procedure; and, allows a validation of the map structure before its usage to optimize the memory management.…”

Section: Related Workmentioning

confidence: 99%

Topological map‐based approach for localization and mapping memory optimization

Aguiar

Santos

et al. 2022

Journal of Field Robotics

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Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.

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Section: Related Workmentioning

confidence: 99%