This page offers insights into LIDAR mapping and navigation techniques vital for autonomous robot movement, particularly within the ROS 2 framework. It introduces ROS packages like move_base and amcl for path planning and robot positioning, alongside methods like DWA_planner and TEB_planner for optimized navigation. A concise guide is provided for launching LIMO navigation, including steps for map alignment correction and setting navigation goals through the RViz interface.

Table of Contents

Two laser mapping methods were used in previous page LiDAR Mapping . Then use the map just built to navigate.

Navigation framework

The key to navigation is robot positioning and path planning. For these, ROS provides the following two packages.

• move_base：achieve the optimal path planning in robot
• amcl：achieve robot positioning in a two-dimensional

On the basis of the above two packages, ROS provides a complete navigation framework.

The robot only needs to publish the necessary sensor information and navigation goal position, and ROS can complete the navigation function. In this framework, the move_base package

provides the main operation and interactive interface of navigation. In order to ensure the

accuracy of the navigation path, the robot also needs to accurately locate its own position. This part of the function is implemented by the amcl package.

1. Move_base package

Move_base is a package for path planning in ROS, which is mainly composed of the following two planners.

• Global path planning (global_planner). Global path planning is to plan the overall path according to a given goal position and global In navigation, Dijkstra or A* algorithm is used

for global path planning, and the optimal route from the robot to the goal position is calculated as the robot's global path.

• Local real-time planning (local_planner). In practice, robots often cannot strictly follow the global So it is necessary to plan the path that the robot should travel in each cycle according to the map information and obstacles that may appear near the robot at any time. So that it

conforms to the global optimal path as much as possible.

2. Amcl package

Autonomous positioning means that the robot can calculate its position on the map in any state. ROS provides developers with an adaptive (or kld sampling) Monte Carlo localization (amcl), which is a probabilistic positioning system that locates mobile robots in 2D. It implements an adaptive

(or KLD-sampling) Monte Carlo localization, using particle filtering to track the pose of the robot on a known map.