Topological maps generated in complex and irregular unknown environments are meaningful for autonomous robots’ navigation. To obtain the skeleton of the environment without obstacle polygon extraction and clustering, we propose a method to obtain high-quality topological maps using only pure Voronoi diagrams in three steps. Supported by Voronoi vertex’s property of the largest empty circle, the method updates the global topological map incrementally in both dynamic and static environments online. The incremental method can be adapted to any fundamental Voronoi diagram generator. We maintain the entire space by two graphs, the pruned Voronoi graph for incremental updates and the reduced approximated generalized Voronoi graph for routing planning requests. We present an extensive benchmark and real-world experiment, and our method completes the environment representation in both indoor and outdoor areas. The proposed method generates a compact topological map in both small- and large-scale scenarios, which is defined as the total length and vertices of topological maps. Additionally, our method has been shortened by several orders of magnitude in terms of the total length and consumes less than 30% of the average time cost compared to state-of-the-art methods.

Most existing template matching algorithms are global matching between the template target and the search region, which makes the matching process retain a lot of unfavourable background information and ignore the structure and local information of the template target. To address this problem, a template matching algorithm based on bipartite graph and graph attention mechanism is proposed in this paper. The algorithm models the similarity matching problem between template features and search region features as a complete bipartite graph, realises local‐to‐local information transfer between the two, and uses the graph attention mechanism to apply weights between local information to obtain a learnable embedding network module. In addition, in terms of feature representation, a multi‐level feature fusion module based on CNN is introduced, which improves the representation of a target by fusing features with different representational meanings of the target. Experimental results on several typical datasets show that the proposed algorithm achieves leading performance in terms of accuracy and efficiency compared to the two state‐of‐the‐art CNN‐based template matching algorithms, Deep‐DIM and QATM.

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