A Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry.

An open-source implementation of MAVIS-SLAM.

This repo is modified based on LIO_SAM, which remove the feature extraction module and makes it easier to adapt your sensor.

A GPU-accelerated TSDF and ESDF library for robots equipped with RGB-D cameras.

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

📍PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency

[SIGGRAPH Asia 2023] MIPSFusion is a neural SLAM method based on multi-implicit-submap representation for scalable online RGB-D reconstruction.

[ICCV2023] NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

[ICCV2023] GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

Instant neural graphics primitives: lightning fast NeRF and more

[RA-L 2023] Official Code Release for LONER: LiDAR Only Neural Representations for Real-Time SLAM

a porting for BundleFusion working on ubuntu, with Pangolin as visualizer

[RA-L'24] DUFOMap: Efficient Dynamic Awareness Mapping

C++ Implementation of PyTorch Tutorials for Everyone

Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson

[CVPR2024] NeuRAD: Neural Rendering for Autonomous Driving

[SIGGRAPH 2023, TPAMI 2024] Code for NeRF-Texture: Texture Synthesis with Neural Radiance Fields

A generic and robust calibration toolbox for multi-camera systems

Vision-Kit provides some tools for your vision/robotics project.

H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation (RAL 2023)

Fast neural radiance field training with free camera trajectories

Lightning fast C++/CUDA neural network framework

stdgpu: Efficient STL-like Data Structures on the GPU

A CUDA implementation of keypoint detection and descriptor extraction

[CVPR'24] Gaussian Splatting SLAM

R2LIVE is a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses the measurement from the LiDAR, inertial sensor, visual camera to achieve robust, accurate state estimation.