ELiOT: End-to-end LiDAR odometry with transformers harnessing real-world, simulated, and digital twin

The development of smart cities depends on intelligent systems that integrate data from diverse environments. In this work, we present ELiOT, an end-to-end LiDAR odometry framework with transformer architecture designed to utilize real-world data, simulations, and digital twins. ELiOT leverages high-fidelity simulators and digital twin environments to enable sim-to-real applications, training on the real-world KITTI odometry dataset while benefiting from simulated data for improved generalization. Our self-attention-based flow embedding network eliminates the need for traditional 3D-2D projections by implicitly modeling motion from sequential LiDAR scans. The framework incorporates a 3D transformer encoder-decoder to extract rich geometric and semantic features. By integrating digital twin environments and simulated data into the training process, ELiOT bridges the gap between simulation and real-world applications, offering robust and scalable solutions for urban navigation challenges. This work underscores the potential of combining real-world and virtual data to advance LiDAR odometry and highlights its role for the future smart cities.