A Novel LiDAR Odometry Based on Surface Distributed Point Feature with Dual Feature Fusion

Abstract

LiDAR odometry has gained popularity due to the LiDAR sensor’s accurate depth measurement and robustness to varying illumination conditions. The feature-based methods achieve the advantage of efficiency through feature extraction, while the distribution-based ones attain better accuracy by modeling the point cloud as distributions. Combining the strengths of both methods is anticipated to yield superior performance. However, existing combination schemes typically extract and process features and distributions separately, and such a loosely integrated approach cannot fully leverage their complementarity. To address this problem, we propose a novel LiDAR odometry method based on surface distributed point (SDP) feature with dual feature fusion. Specifically, the SDP feature is introduced to tightly integrate features and distributions, facilitating efficient feature association and map maintenance. On this basis, the associated source and target features are then effectively integrated through dual feature fusion to form the dual-fusion (DF) associated plane. This plane serves as the basis for constructing the point-to-DF associated plane constraint for pose optimization. As a result, the local planar structure is more accurately reflected, thereby enhancing the accuracy of pose estimation. The SDP feature and the resultant constraint are employed in both scan-to-map matching and fixed-lag smoothing, which are hierarchically organized to achieve accurate pose estimation. Experiments on KITTI dataset and large-scale KITTI-360 dataset demonstrate the effectiveness of the proposed method.