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Found 6 papers, 4 papers with code
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction
The accurate LiDAR point clouds enable a trained neural signed distance field to offer a manifold geometry field, This motivates us to offer an SDF-based Gaussian initialization for physically grounded primitive placement and a comprehensive geometric regularization for geometrically consistent rendering and reconstruction.
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping
Our map system comprises a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based odometry.
Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems
We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance.
FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization.
Robotics
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars' unique features.
Robotics
