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] 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

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

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

Factored inference for discrete-continuous smoothing and mapping.

Offical code release for DynoSAM: Dynamic Object Smoothing And Mapping [Submitted TRO 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.

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.

learning and feeling SLAM together with hands-on-experiments

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

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".

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