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Lost in the Woods? Place Recognition for Navigation in Difficult Forest Environments
James Garforth1, Barbara Webb1
1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
Frontiers in Robotics and AI
|January 27, 2021
Summary
This study tested NetVLAD for forest mapping, finding it outperforms other loop closure methods. However, integrating it with SLAM systems still faces challenges in resolving matched places for automated conservation.
Area of Science:
- Computer Vision
- Robotics
- Environmental Science
Background:
- Forest environments pose significant challenges for computer vision due to complex textures, dynamic lighting, and high mobility.
- Place recognition, specifically loop closure, is vital for robotic mapping in forests for conservation automation.
Purpose of the Study:
- To evaluate the generalization capability of the NetVLAD deep learning model in forest environments.
- To assess the performance of NetVLAD against existing state-of-the-art loop closure techniques.
- To investigate the integration of NetVLAD with SLAM systems for forest mapping.
Main Methods:
- The study employed the NetVLAD model, a Convolutional Neural Network (CNN)-based place recognition system.
- NetVLAD's performance was compared against other loop closure approaches in forest datasets.
- NetVLAD was integrated with the ORBSLAM2 system and evaluated on a newly created forest dataset.
Main Results:
- NetVLAD demonstrated superior performance compared to state-of-the-art loop closure methods in forest environments.
- While NetVLAD could identify suitable loop closure locations, the integrated SLAM system struggled to resolve feature correspondences for matched places.
Conclusions:
- NetVLAD shows promise for place recognition in challenging forest environments, outperforming existing methods.
- Further research is needed to address the limitations in feature correspondence resolution when integrating NetVLAD with SLAM systems for robust forest mapping and conservation.
