This repository lays out a detailed example code to use the GTSAM Library, used for Factor Graph Optimization of 2-Dimensional and 3-Dimensional trajectory sensor data.

This is a comprehensive project focused on implementing popular algorithms for state estimation, robot localization, 2D mapping, and 2D & 3D SLAM. It utilizes various types of filters, including the Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter.

Node to process Point Cloud Data of the Radar to obtain the Body Frame Velocity

pySLAM-D is a real-time SLAM algorithm for UAV aerial stitching. Includes additional features and refactored code inspired by BU's implementation https://github.com/armandok/pySLAM-D

An application for 3D mapping tasks uses object-based Visual SLAM

SRSLAM: SCUT RobotLab Simultaneous Localization And Mapping

Pure python implementation of minisam.

Implementation of SLAM using the GTSAM library

This repository has some example codes of pose graph optimization of a pose gragh based on GTSAM.

A real-time LiDAR SLAM package that integrates FLOAM and ScanContext.

Graph-based SLAM tutorial

Simple ROS node for fusing lidar- and visual odometries with GTSAM

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

Robot location estimator with graph optimization for sensor fusion. Project for course "Artificial Intelligence in Robotics"

Code release for "The Importance of Coordinate Frames in Dynamic SLAM", ICRA 2024

Car-like vehicle path planning using graph optimization - GTSAM lib

SimpleSfM

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

Apriltag Tag Bundle Calibration Tool