ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

Abstract

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.

Efficient Structure from Motion for Large-Size Videos from an Open Outdoor UAV Dataset

“…This type of study addresses continuous feature tracking and mapping on coherent, densely sampled image sequences. Specifically, VSLAM methods have been developed to estimate camera trajectories and reconstruct scene structures from video streams in real time [7][8][9][10]35]. However, these methods often prioritize speed and, as a result, face limitations when processing large-size high-resolution images.…”

“…Modern unmanned aerial vehicles (UAVs) equipped with cameras have become crucial in several fields, such as surveying and mapping, geographic information systems (GIS), and digital city modeling. To achieve accurate localization and create 3D representations of real-world scenes, techniques like image or video-based structure from motion (SfM) and visual simultaneous localization and mapping (VSLAM) are utilized [1][2][3][4][5][6][7][8][9][10]. However, it is important to note that there is a relatively limited amount of research on large-size videobased SfM specifically designed for outdoor UAVs.…”