| Summary: | This paper proposes a Graph Neural Network
(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration
candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static
graph structure informs our attention-based node aggregation
network by identifying semantic-instance relationships, acting
as an inductive bias to significantly reduce the computational
burden of pointcloud registration. By connecting candidate
nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences
and estimate the displacement between pointcloud scans. Our
pipeline enables introspective analysis of the model’s performance by correlating it with the individual contributions of
local structures in the environment, providing valuable insights
into the system’s behaviour. We test our method on the KITTI
odometry dataset, achieving competitive accuracy compared
to benchmark methods and a higher track smoothness while
relying on significantly fewer network parameters.
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