Topological map-based approach for localization and mapping memory optimization

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Aguiar, AS (Author) Other; Filipe Neves Santos (Author) FEUP; Santos, LC (Author) Other; Armando Jorge Sousa (Author) FEUP; Boaventura Cunha, J (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF FIELD ROBOTICS

Vol. 40

Pages: 447-466

ISSN: 1556-4959

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00X-KMT

DOI: 10.1002/rob.22140

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20