Search

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality. [ABSTRACT FROM AUTHOR]

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis–Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant. [ABSTRACT FROM AUTHOR]

• A new NAH method for low measuring sampling rate was proposed. • Feature extraction and pattern learning capability of deep learning technologies was utilized. • The frequency dimension information of the signal was fully utilized. • A data processing method for the proposed NAH was given. • The proposed NAH method was verified more accurate than the existing methods. Near-field acoustical holography (NAH) is an efficient noise diagnosis method with the deficiencies including wraparound error, which will increase when the spatial sampling rate is reduced below the minimum specified by Shannon-Nyquist theorem. Based on 3D convolutional neural network (3D-CNN) and stacked autoencoder (SAE), a method called CSA-NAH is proposed to reduce the wraparound error under sparse measuring. Subsequently, numerical calculations are carried out to illustrate the feasibility and performance of CSA-NAH. The results show that when holographic measurement point number is 64, average reconstruction error of CSA-NAH on an aluminum plate for sound pressure within 2000 Hz is 4.32%, while the latest existing methods is greater than 10%. For error in 1200 Hz~2000 Hz, the error is reduced from more than 15% of the existing methods to 5.5%. Therefore, the application of the proposed CSA-NAH can cut down the measuring cost by reducing the number of microphones without wraparound error. [ABSTRACT FROM AUTHOR]