360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network

To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera's narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance.

Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a data-driven technique to forecast this parameter at Tabriz in East Azerbaijan. Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture whilst DPT is the model's output. The findings indicate that, in general, ANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the data-driven technique (machine learning method) and the observed data. Using this prediction toolkit, DPT can be adequately forecasted solely based on the temperature distribution of Tabriz. This kind of modeling is extremely promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales. Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT.