Localization and Mapping Using MATLAB

Abstract

In robotics and autonomous systems, localization and mapping are essential responsibilities. Whereas mapping seeks to construct or update the map using sensor data, localization aims to estimate the robot's pose (i.e. position and orientation) relative to the map. An overview of localization and mapping methods using MATLAB is provided in this article. The quality of data utilized for localization and mapping is affected by sensor models and their characteristics, such as noise and resolution, which are covered in the first section of the article. Then, other localization strategies are covered, including odometry, landmark-based techniques, and probabilistic techniques like particle filters and Kalman filters. Each method's benefits and drawbacks are thoroughly explained. Following that, the topic concentrates on mapping, which is producing or updating a map using sensor data. It covers two primary categories of mapping methods: feature-based mapping and occupancy grid mapping. In occupancy grid mapping, the environment is divided into a grid of cells; with each cell receiving a probability value that indicates how likely it is that it will be occupied. The process of feature-based mapping entails locating and monitoring recognizable environmental elements like corners and edges. The page also provides MATLAB code-based localization and mapping examples and demonstrations. These examples cover subjects including occupancy grid mapping using a laser range finder, posture estimation using odometry, and landmark-based localization utilizing Simultaneous Localization and Mapping (SLAM). This article is appropriate for anyone with an interest in robotics, autonomous systems, or computer vision because it offers a thorough review of localization and mapping methods using MATLAB.