SLAM

Simultaneous Localization and Mapping(SLAM) examples Simultaneous Localization and Mapping (SLAM) is an ability to estimate the pose of a robot and the map of the environment at the same time. The SLAM problem is hard to solve, because a map is needed for localization and localization is needed for mapping. In this way, SLAM is often said to be similar to a ‘chicken-and-egg’ problem. Popular SLAM solution methods include the extended Kalman filter, particle filter, and Fast SLAM algorithm[31]. Fig.4 shows SLAM simulation results using extended Kalman filter and results using FastSLAM2.0[31].

Contents

• Iterative Closest Point (ICP) Matching
  • References
• EKF SLAM
  • Simulation
  • Introduction
  • Algorithm Walk through
  • 1- Predict
  • 2 - Update
  • Observation Step
  • References:
• FastSLAM1.0
  • Simulation
  • Introduction
  • Algorithm walk through
  • 1- Predict
  • 2- Update
  • 3- Resampling
  • References
• FastSLAM 2.0
  • References
• Graph based SLAM
  • Graph SLAM
  • Graph SLAM Formulation
  • Graph SLAM for a real-world SE(2) dataset
  • References: