I2D-Loc++: Camera Pose Tracking in LiDAR Maps With Multi-View Motion Flows

Abstract

Camera localization in LiDAR maps has become increasingly popular due to its promising ability to handle complex scenarios, surpassing the limitations of visual-only localization methods. However, existing approaches mostly focus on addressing the cross-modal 2D–3D gaps while overlooking the relationship between adjacent image frames, which results in fluctuations and unreliability of camera poses. To alleviate this, we introduce a novel camera pose tracking framework in LiDAR maps by coupling the 2D–3D correspondences with 2D–2D feature matching (I2D-Loc++), which establishes the multi-view geometric constraints to improve localization stability and trajectory smoothness. Specifically, the framework consists of a front-end hybrid flow estimation network and a non-linear least square pose optimization module. We further design a cross-modal consistency loss to integrate the multi-view motion flows for the network training and the back-end pose optimization. The pose tracking model is trained on the KITTI odometry dataset, and tested on the KITTI odometry, Argoverse, Waymo and Lyft5 datasets, which demonstrates that I2D-Loc++ has superior performance and good generalization ability in improving the accuracy and robustness of camera pose tracking.