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.

翻译：本文提出一种基于图神经网络（GNN）的方法，利用语义和局部几何信息指导可靠点云配准候选点的识别。环境中语义与形态特征作为配准的关键参考点，实现基于激光雷达的高精度位姿估计。我们设计的新型轻量级静态图结构通过识别语义实例关系，为基于注意力的节点聚合网络提供信息，作为归纳偏置显著降低点云配准的计算负担。通过连接候选节点并利用跨图注意力机制，我们为所有潜在配准对应关系赋予置信度分数，并估计点云扫描之间的位移。本方法通过将模型性能与环境局部结构的个体贡献相关联，实现对模型性能的内省分析，为系统行为提供深度洞察。我们在KITTI里程计数据集上验证了该方法，在与基准方法相当的精度和更高轨迹平滑度的前提下，参数量大幅减少。