Abstract
Static environment is a prerequisite for most of visual simultaneous localization and mapping systems. Such a strong assumption limits the practical application of most existing SLAM systems. When moving objects enter the camera’s view field, dynamic matching points will directly interrupt the camera localization, and the noise blocks formed by moving objects will contaminate the constructed map. In this paper, a semantic SLAM system working in indoor dynamic environments named Blitz-SLAM is proposed. The noise blocks in the local point cloud are removed by combining the advantages of semantic and geometric information of mask, RGB and depth images. The global point cloud map can be obtained by merging the local point clouds. We evaluate Blitz-SLAM on the TUM RGB-D dataset and in the real-world environment. The experimental results demonstrate that Blitz-SLAM can work robustly in dynamic environments and generate a clean and accurate global point cloud map simultaneously.
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