Outdoor Environment Reconstruction with Deep Learning on Radio Propagation Paths

arXiv:2402.17336v1 Announce Type: cross
Abstract: Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, and high computational and energy demands. These challenges are particularly pronounced in applications like augmented reality navigation, especially when integrated with wearable devices featuring constrained computational resources and energy budgets. In response, this paper proposes a novel approach harnessing ambient wireless signals for outdoor environment reconstruction. By analyzing radio frequency …

abstract applications arxiv augmented reality challenges computational coverage cs.lg cs.ni deep learning eess.sp energy environment environmental lidar limitations navigation photogrammetry propagation radio reality type vision