EVP-LCO: LiDAR-Camera Odometry Enhancing Vehicle Positioning for Autonomous Vehicles

As an emerging application of the Internet of Things (IoT), autonomous vehicles (AVs) has attracted widespread attention from scholars. Accurate vehicle positioning is crucial for AVs to navigate safely and efficiently. Among the key components of positioning systems, odometry plays a vital role in tracking the vehicle’s movement, especially when GPS is unavailable. Traditional single-modal odometry methods, which rely solely on either LiDAR or cameras, often experience limited accuracy in challenging environmental or weather conditions. Therefore, some researchers proposed an idea of combining information from both LiDAR and cameras, called LiDAR-camera odometry (LCO). However, the existing LCO methods face issues like insufficient data integration or complexity in system structure. To address these challenges, we propose a novel LCO method named EVP-LCO, which enhances the sparse features from LiDAR by pseudo-LiDAR. In our method, we use a data augmentation module to enrich the details of LiDAR point cloud. Furthermore, we devise a feature regrouping strategy in two-step Levenberg-Marquardt (LM) optimization process to estimate accurate pose and reconstruct colorful global map. The results on the KITTI Odometry dataset show that EVP-LCO significantly improves vehicle positioning accuracy.