Generative Adversial Networks for Enhanced Visual Localization in Autonomous Systems
Keywords:
Localization, Autonomous systems, GAN, Binarized Spiking Neural Networks, KITTIAbstract
Autonomous system localization is pivotal for determining their position within their environment. While the Global Positioning System (GPS) is a widely used method, its limitations, such as imprecise pose estimation, necessitate alternative approaches. Visual localization is one such approach that localize the system with images captured by cameras, offers a promising solution. In this research, we employ Generative Networks and Deep Learning techniques to calculate Autonomous system positions relative to the world. Landmarks are detected using generative networks, and the autonomous system is localized using binarized spiking neural networks based on the identified landmarks. The proposed model achieves a mean Intersection over Union (mIoU) score of 0.85, showcasing a 6.25% improvement over existing models. The presented framework enhances system localization accuracy, minimizing pose errors in both outdoor environments and GPS-denied locations.
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