LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

LIO_SAM for 6-axis IMU and GNSS.

Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.

The tight integration of FAST-LIO with Radius-Search-based loop closure module.

ROS 2 package of tightly-coupled lidar inertial ndt/gicp slam

For an education purpose, from-scratch, single-file, python-only pose-graph optimization implementation

[TMECH'2024] Official codes of ”PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation“

A collection of GTSAM factors and optimizers for point cloud SLAM

[ICRA@40] MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System

Visual Inertial Odometry (VIO) / Simultaneous Localization & Mapping (SLAM) using iSAM2 framework from the GTSAM library.

Factored inference for discrete-continuous smoothing and mapping.

Robust GNSS Processing With Factor Graphs

Lightweighted graph optimization (Factor graph) library.

learning and feeling SLAM together with hands-on-experiments

LIO-SAM-6AXIS with intensity image loop optimization

The full_linear_wheel_odometry_factor provides motion constraints and online calibration for skid-steering robots. This constraint can be incorporated into your SLAM framework. Here is an example video using this factor. https://youtu.be/Vss86xUhU80

Software Release for "Incremental Covariance Estimation for Robust Localization"

Code release for "Evaluation of Precise Point Positioning Convergence with an Incremental Graph Optimizer".

Lidar localization system with prior map constraint and lio constraint based on GTSAM

IMU-based human skeletal pose estimation in C++11