At present, indoor location-based service has become a hot topic. The traditional approaches that rely on professional equipment and expertise to collect indoor maps has limited the rapid development of indoor location-based services. Adopting low-cost crowdsourcing methods to construct and maintain indoor maps with various sensors integrated in smart phones by common users is a development trend of constructing indoor map. The existing crowdsourcing-based indoor topology construction methods mostly use the pedometer based PDR (Pedestrian Dead Reckoning) to infer user trajectories, which suffers from the problem of accumulated estimation errors. To reduce the error of pedestrian dead reckoning and improve the accuracy of the indoor topology construction, we propose a novel indoor topology construction algorithm based on the user closed trajectory fusion. The algorithm requires the user to start and end a trajectory loop at the same location when collecting data. Based on this trajectory loop, we can perform the forward and backward dead reckoning starting from the starting and the ending point of the path, respectively. For instance, after completing the forward path inference, we can swap the starting point with the ending point, and then perform the reverse path inference. Last, we aggregate the coordinates obtained from the forward and reverse path inferences with the different weight. The weight is defined to be proportional to the confidence of the PDR, i.e., inversely proportional to the walking time from the starting location of path inference. Compared to the traditional unidirectional trajectory inference method, our proposed algorithm can greatly reduce the accumulated error of dead reckoning by aggregating the bidirectional trajectory inference. To improve the accuracy of heading estimation of each step, we Ieverage a radial Gaussian function to filter the compass noise, and adopt the multi-strategy fusion mechanism based on the difference of direction es...