6 accepted papers
The growing demand for efficient image segmentation in marine ecological studies is currently constrained by two key factors: the high computational requirements of models such as the Segment Anything Model (SAM) and the degraded accuracy of lightweight models in underwater environments. To overcome
Utilizing 2D images for place recognition within 3D point cloud maps presents significant challenges in autonomous driving applications, primarily due to the inherent cross-modal disparity between visual and LiDAR data. In this study, we propose a novel cross-modal visual-LiDAR place recognition met
This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinemen…
Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately…
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficie…
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse…