Topology Aware Object-Level Semantic Mapping Towards More Robust Loop Closure

Loop closure can effectively eliminate the accumulated error and plays an important role in Simultaneous Localization and Mapping (SLAM). There remains challenges in loop detection and loop correction due to the large viewpoints difference and the environment appearance changes. In this letter, we propose a novel loop closure method based on object modeling and semantic graph matching. We use voxels and cuboids to model the object-level features in the environment, and further represent the environment as a semantic graph with topological information. On this basis, an efficient graph matching method based on edit distance is proposed for robust place recognition. Finally, the loop correction is carried out by object alignment between semantic maps. Experimental results demonstrate that the proposed method can realize omni-directional loop closure without viewpoint constraint, and is robust to environmental appearance changes. Compared with the existing appearance-based methods, the performance has been greatly improved