Simultaneous Localization and Mapping(SLAM) examples

This is a 2D ICP matching example with singular value decomposition.

It can calculate a rotation matrix and a translation vector between points to points.

Ref:

• Introduction to Mobile Robotics: Iterative Closest Point Algorithm

This is an Extended Kalman Filter based SLAM example.

The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with EKF SLAM.

The green crosses are estimated landmarks.

Ref:

• PROBABILISTIC ROBOTICS

This is a feature based SLAM example using FastSLAM 1.0.

The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM.

The red points are particles of FastSLAM.

Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.

Ref:

• PROBABILISTIC ROBOTICS
• SLAM simulations by Tim Bailey

This is a feature based SLAM example using FastSLAM 2.0.

The animation has the same meanings as one of FastSLAM 1.0.

Ref:

• PROBABILISTIC ROBOTICS
• SLAM simulations by Tim Bailey

This is a graph based SLAM example.

The blue line is ground truth.

The black line is dead reckoning.

The red line is the estimated trajectory with Graph based SLAM.

The black stars are landmarks for graph edge generation.

Ref:

• A Tutorial on Graph-Based SLAM