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Conference Paper
Pose360: Metric-Scale Visual Odometry by Grounding Learned Features with LiDAR Depth
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- Authors
- Kemal Mudie Tosora, Seher Kanwal, Seung-Ik Lee
- Issue Date
- 2026-02
- Citation
- International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2026, pp.1438-1442
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Metric-scale state estimation is a cornerstone for autonomous systems, yet single-modality solutions often fail in challenging real-world environments. Visual odometry suffers from inherent scale ambiguity, while LiDAR odometry can be fragile in geometrically sparse scenes. To overcome these limitations, we propose Pose360, a novel Visual-LiDAR Odometry (V-LIO) system that robustly fuses a 360° panoramic camera and a 360° LiDAR. Our approach leverages learned features, SuperPoint and LightGlue, to establish strong 2D visual correspondences, which are then lifted into a sparse set of metric 3D-3D point correspondences using depth information from a synchronized LiDAR point cloud. The 6-DoF relative pose is then computed efficiently via a closed-form SVD-based solution. This focused fusion strategy directly resolves visual scale ambiguity without requiring complex non-linear optimization. We perform a rigorous quantitative evaluation on a challenging 450 m real-world dataset, demonstrating that our system achieves high global consistency with a translation Absolute Trajectory Error (ATE) of just 0.177 m RMSE against a high-fidelity LiDAR SLAM ground truth. We further validate its real-world applicability by successfully integrating Lidar360Pose as the core odometry engine in a full robotic navigation stack, proving it is an accurate and reliable solution for metric state estimation.
- Keyword
- Visual Odometry, Sensor Fusion, Pose Estimation, LiDAR, Panoramic Camera, Autonomous Systems
- KSP Keywords
- Autonomous Systems, Depth information, Fusion Strategy, Global consistency, High-fidelity, LiDAR point cloud, Panoramic camera, Point correspondences, Pose estimation, Quantitative evaluation, Real-world