Research Papers

LeGO-LOAM introduces a cutting-edge lidar odometry and mapping framework designed to deliver real-time, accurate 6-DOF pose estimation for ground vehicles, optimized for challenging, variable terrain environments. It significantly reduces computational

Reliable-loc introduces a resilient LiDAR-based global localization system for wearable mapping devices in complex, GNSS-denied street environments with sparse features and incomplete prior maps. Key Highlights: Paper Resources Related articles

This is the world’s first SLAM dataset recorded onboard real roller coasters, offering extreme motion dynamics, perceptual challenges, and unique conditions for benchmarking SLAM algorithms under aggressive real-world trajectories. Key

This paper introduces a SLAM framework that achieves real-time CPU-only performance in dense, registration-error-minimization-based odometry and mapping by leveraging exact point cloud downsampling via coreset extraction, eliminating the need for

MP-SfM redefines classical Structure-from-Motion by tightly integrating monocular depth and surface normal priors into incremental SfM, enabling robust 3D reconstruction from sparse, unstructured image collections. Key Highlights: Resources Paper: https://arxiv.org/abs/2504.20040Github:

NormalCrafter introduces a novel approach for surface normal estimation in videos, leveraging diffusion priors to achieve high spatial fidelity and temporal consistency over arbitrary-length sequences. Key Highlights: Project Related articles

OpenLiDARMap presents a GNSS-free mapping framework that combines sparse public map priors with LiDAR data through scan-to-map and scan-to-scan alignment. This approach achieves georeferenced and drift-free point cloud maps. Key

 MedSAM2 introduces a robust foundation model for promptable segmentation in 3D medical images and temporal video data, built by fine-tuning SAM2.1 on a large-scale curated medical dataset. Key Highlights: Resources

GenZ-ICP introduces an innovative iterative Closest Point (ICP) method that enhances LiDAR-based pose estimation by adaptively integrating point-to-plane and point-to-point error metrics, ensuring robust performance across diverse and degenerative environments.