Mapping

Robot Operating System (ROS)

ROS.org

ROS Fuerte

ROS - Robot Operating System is an open-source, meta-operating system for your robot. It provides the services you would expect from an operating system, including hardware abstraction, low-level device control, implementation of commonly-used functionality, tools for visualization, message-passing between processes and package management, see http://www.ros.org. ROS is licensed under an open source, BSD license.

The RRT-Team use the release ROS Fuerte (http://www.ros.org/wiki/fuerte/Installation/Ubuntu). A basic ROS installation for RoboCup Rescue was developed at the followingworkshops and Summer schools: Workshop on Standard Robotic Software Architecture for RoboCup Rescue based on ROS in Koblenz, Germany (2011) ROS Robocup Rescue Summerschool in Graz, Austria (2012). SSRR Summer School in Alanya, Turkey (2012).

Information: ROS, ROS Fuerte, RoboCup Rescue-ros-pkg


2D-Mapping / SLAM

One of the most important tasks at the RoboCupRescue is to explore an unknown terrain and create a map of this terrain. This leads to the common known SLAM (Simultaneous Localization and Mapping) problem. To solve the SLAM problem hector_slam is used by the RRT-Team.
Hector_slam consists of several ROS (Robot Operating System) packages. One node of these packages is the hector_mapping node

Fig. 1: Hector-SLAM

“Hector_mapping is a SLAM approach that can be used without odometry as well as on platforms that exhibit roll/pitch motion (of the sensor, the platform or both). It leverages the high update rate of modern LIDAR systems like the Hokuyo UTM-30LX and provides 2D pose estimates at scan rate of the sensors (40Hz for the UTM-30LX). While the system does not provide explicit loop closing ability, it is sufficiently accurate for many real world scenarios. The system has successfully been used on Unmanned Ground Robots, Unmanned Surface Vehicles, Handheld Mapping Devices and logged data from quadrotor UAVs and is developed from TU Darmstadt.

It creates an occupancy grid map using a LIDAR (Light Detecting and Ranging) System. The grid consists of cells which store the information if they are free space, an obstacle or unknown terrain. If the Robot starts exploration the map consists of only unknown terrain cells. If after a few scans the Robot detected obstacles it remarks the information in these cells. The cells before an obstacle are remarked as free space. The cells behind an obstacle stay unknown terrain. Before and behind are always related on a direct line to the center of the laser (the way of the laser beam).

Information: TU Darmstadt-ros-pkg


3D-Mapping

Fig. 2: 3D-Mapping

A popular approach to modeling environments in 3D is to use a grid of cubic volumes of equal size (voxels) to discretize the mapped area. During the ROS Summer School in Graz (2012) the researchers have developed a 3D-Mapping software. The software stack is based on the OctoMap, which was developed as a probabilistic, flexible and compact 3D Map representation for robotic systems from the University of Freiburg, Department of Computer Science.

Information: Octomap