Search Results for author:
Found 2 papers, 1 papers with code
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition methods.
Towards Multidimensional Textural Perception and Classification Through Whisker
Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2. 5\mu m$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
