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Journal Article
ELiOT: End-to-end LiDAR odometry with transformers harnessing real-world, simulated, and digital twin
- Authors
- Daegyu Lee, Hyunwoo Nam, Insung Jang, David Hyunchul Shim
- Issue Date
- 2025-10
- Citation
- ETRI Journal, v.47, no.5, pp.815-829
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Digital Twin, Encoder and Decoder, End to End(E2E), High fidelity, Real-world applications, Real-world data, Simulated data, Smart city, Urban navigation, Virtual Data, intelligent systems
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
