gtsam

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

[TMECH'2024] 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

Offical code release for DynoSAM: Dynamic Object Smoothing And Mapping. Accepted Transactions on Robotics (Visual SLAM SI). A visual SLAM framework and pipeline for Dynamic environements, estimating for the motion/pose of objects and their structure, as well as the camera odometry and static map.

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

Factored inference for discrete-continuous smoothing and mapping.

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

Robust GNSS Processing With Factor Graphs

Lightweighted graph optimization (Factor graph) library.

Offline manual loop closure editing and optimization tools for LiDAR mapping pose graphs. 用于激光雷达建图位姿图的离线手动闭环编辑与优化工具。

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

learning and feeling SLAM together with hands-on-experiments

LIO-SAM-6AXIS with intensity image loop optimization

Software Release for "Incremental Covariance Estimation for Robust Localization"

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