An improved semantic segmentation and fusion method for semantic SLAM

Perception of the environment is an important part of robot intelligence. In order to better interact with the environment, the robot should not only know the shape of objects but also their semantics. In order to meet diversified needs, robot products are becoming more and more miniaturized, and related technologies have become research hotspots in the field. In response to this situation, this paper focuses on speed optimization based on the existing semantic map construction method to make it suitable for operation in embedded systems. This paper makes improvements to semantic segmentation and uses TensorRT to build a fast inference engine to accelerate target detection and speed up its inference speed on embedded devices. This paper uses Bayesian fusion method to fuse the semantic information of different locations to build an accurate map. Finally, in order to evaluate the real-time performance and effectiveness of this method, a test on the ADE20K data set was carried out, and the experimental results were analyzed to prove the effectiveness of the optimization of this algorithm.