In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based Deep Learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control.
3-dimensional, accurate, and up-to-date maps are essential for vehicles with autonomous capabilities, whose functionality is made possible by machine learning-based algorithms. Since these solutions require a tremendous amount of data for parameter optimization, simulation-to-reality (Sim2Real) methods have been proven immensely useful for training data generation. For creating realistic models to be used for synthetic data generation, crowdsourcing techniques present a resource-efficient alternative. In this paper, we show that using the Carla simulation environment, a crowdsourcing model can be created that mimics a multi-agent data gathering and processing pipeline. We developed a solution that yields dense point clouds based on monocular images and location information gathered by individual data acquisition vehicles. Our method provides scene reconstructions using the robust Structure-from-Motion (SfM) solution of Colmap. Moreover, we introduce a solution for synthesizing dense ground truth point clouds originating from the Carla simulator using a simulated data acquisition pipeline. We compare the results of the Colmap reconstruction with the reference point cloud after aligning them using the iterative closest point algorithm. Our results show that a precise point cloud reconstruction was feasible with this crowdsourcing-based approach, with 54\% of the reconstructed points having an error under 0.05 m, and a weighted root mean square error of 0.0449 m for the entire point cloud.
Weather conditions have both direct and indirect effects on road traffic. On the one hand, precipitation heavily influences road traction as well as vehicle visibility, and on the other hand, medical meteorology phenomena -for example weather fronts -have a great effect on the driving ability and accuracy of drivers. Weather fronts -through their physiological effects -may cause the deterioration of human reaction times, and therefore increase the risk of accidents' occurrence in unexpected and hazardous traffic situations that would require quick intervention of drivers. In this article, our research regarding the relation between weather fronts and traffic accidents as well as between local meteorological parameters and weather fronts is presented.In this article, a crowdsourcing-based methodology is presented, that is supported by a database, which is assembled using data that is far more heterogeneous with regard to acquisition frequency and quality, than those available previously. Using such a database, a driver aiding system can be created for use in cars currently supporting lower levels of autonomy. Such driver assistance systems would be capable of informing or, in a given situation, indirectly decrease the likelihood of the development of an emergency by warning the driver.
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