[TMECH'2024] Official codes of the paper. PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
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Updated
Aug 27, 2024 - C++
[TMECH'2024] Official codes of the paper. PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
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
A collection of GTSAM factors and optimizers for point cloud SLAM
This repository has some example codes of pose graph optimization of a pose gragh based on GTSAM.
MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System
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.
: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
Code release for "The Importance of Coordinate Frames in Dynamic SLAM", ICRA 2024
LIO_SAM for 6-axis IMU and GNSS.
Pure python implementation of minisam.
Pratice and example to solve the pose graph SLAM problem using GTSAM
IMU-based human skeletal pose estimation in C++11
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