HMAPs - Hybrid Height- Voxel Maps for Environment Representation

Garrote, Luís; Premebida, Cristiano; David, Sina; Nunes, Urbano

doi:10.1109/iros.2018.8594113

Cited by 4 publications

(2 citation statements)

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“…The evaluation consists in comparing the generated paths from the AMCL (only), AMCL-ORCHANet, SLAM, and RTK-GPS. First, an HMaps-based SLAM approach as proposed in [18] was used to create an environment representation of the autumn sequence (see Fig. 7).…”

Section: E Evaluation On Localization Frameworkmentioning

confidence: 99%

AttDLNet: Attention-Based Deep Network for 3D LiDAR Place Recognition

Barros

Garrote

Pereira

et al. 2022

Lecture Notes in Networks and Systems

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Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to structural similarity across the entire field. In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness. Hence, we propose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans to global descriptors. Specifically, this work proposes a new global feature aggregation approach, which fuses multiple aggregation methods into a robust global descriptor. ORCHNet is evaluated on real-world data collected in orchards, comprising data from the summer and autumn seasons. To assess the robustness, We compare ORCHNet with state-ofthe-art aggregation approaches on data from the same season and across seasons. Moreover, we additionally evaluate the proposed approach as part of a localization framework, where ORCHNet is used as a loop closure detector. The empirical results indicate that, on the place recognition task, ORCHNet outperforms the remaining approaches, and is also more robust across seasons. As for the localization, the edge cases where the path goes through the trees are solved when integrating ORCHNet as a loop detector, showing the potential applicability of the proposed approach in this task. The code and dataset will be publicly available at:https://github.com/Cybonic/ORCHNet.git

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Section: E Evaluation On Localization Frameworkmentioning

confidence: 99%

AttDLNet: Attention-Based Deep Network for 3D LiDAR Place Recognition

Barros

Garrote

Pereira

et al. 2022

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The next works are going to be classified according to the structures created for mapping. Firstly, we present works that built both a global metric map and a global topological map [16,67,83,179,180,186]. In some works, such as the work by Zhang [179], a global metric map is built and afterwards a topological map is extracted from the metric structure.…”

Section: Figure 21mentioning

confidence: 99%