Graph Neural Network Based Semantic Mapping and Classification of Dataset for Robotics Applications

Abstract

In the field of Robotics, deriving meaningful insights from spatial information ispivotal. Our objective was to work with 2D dataset as 3D datasets are relativelymore time consuming as our target is to objective was to do the foundation workfor real time inferences where speed is also an important key factor and so to getbest possible results, trying to improvise in accuracy and speed. Our focus liesin semantic mapping, semantic place classification where mobile robots interpretpartial, noisy sensory data. We delve into end-to-end techniques rooted in probabilisticdeep networks, studying Local-SPNs, Graph SPNs, and TopoNets. Additionally,we explore GNNs for semantic place classification. Our report providessuccinct insights into these methods, emphasizing their principles and implementations,including the local SPN model and GNN for semantic classification. Wegot our best accuracy with GNN 70.15% which is less compared to previous best80.14% but we achieved better results with speed as our GNN model was 1.89xfaster than the previous best avilable method. We also explore multi-level semanticplace classification through GNNs and consider the potential of GraphAttention Networks (GAT) for complex datasets. Here we did a proof of conceptthat multilevel semantic classification is possible with GNNs but it needs moreresearch in this area.