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

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

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

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

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.

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

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

:squirrel: GTSAM meets catkin

Factored inference for discrete-continuous smoothing and mapping.

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

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

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

State Estimation of a Differential Robot using Factor Graphs

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

LIO_SAM for 6-axis IMU and GNSS.

SimpleSfM

Pure python implementation of minisam.

Apriltag Tag Bundle Calibration Tool