Real-Time Semantic Aided Place Recognition for Loop Closure Detection

S. Babu

A SLAM system requires effective loop closure detection to reduce the errors in the map and create a consistent map. Currently, GET Lab uses a loop closure detection approach that encodes low-level features as the global descriptors to represent scenes. Due to the difficulty in generating effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Inspired by the perspective of humans, who recognize scenes through identifying semantic objects and capturing their relations. This study explores the use of high-level features, namely semantics, to improve the descriptor's capability to represent scenes. Further proposes a semantic graph representation to encode the semantic and topological information of the raw point cloud. Place recognition is modeled as a graph matching problem and then a graph similarity network is used to compute the similarity. The new method should run in real time and can be integrated into the existing SLAM system. The evaluations will be tested in real-world indoor and large urban outdoor environments at Paderborn University.