  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.
