In simultaneous localization and mapping (SLAM) system, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the precise self-localisation of the robot; however, the performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. In the traditional likelihood matching methods, the number of matching words and the difference between them are not considered. This paper proposes a method based on mixed similarity to calculate the similarity score, thereby improving the performance of closed-loop detection. Experiments are performed on datasets from dynamic environments and visual repetitive environments, and then this method can produce a higher recall rate with 100% accuracy compared to the latest methods.
Considering the condition that cash and time are constrained, and the general background of growing financing cost, aiming at maximizing the net present value (NPV), an optimizing model for this kind of problem is established. Ant colony optimization is modified to solve above problem, an elitist strategy is introduced to accelerate convergence. A specific example of this problem is utilized to validate above model and its algorithm. It indicates from the result that the model can provide a dispatch strategy of the project, the algorithm provide a satisfactory solution for the problem. The convergence of the algorithm is relatively good and it can be used to solve practical problems.
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