|This article does not cite any references or sources. (December 2009)|
Robotic mapping is a discipline related to cartography. The goal for an autonomous robot is to be able to construct (or use) a map or floor plan and to localize itself in it. Robotic mapping is that branch of one, which deals with the study and application of ability to construct map or floor plan by the autonomous robot and to localize itself in it.
Evolutionarily shaped blind action may suffice to keep some animals alive. For some insects for example, the environment is not interpreted as a map, and they survive only with a triggered response. A slightly more elaborated navigation strategy dramatically enhances the capabilities of the robot. Cognitive maps enable planning capacities, and use of current perceptions, memorized events, and expected consequences.
The robot has two sources of information: the idiothetic and the allothetic sources. When in motion, a robot can use dead reckoning methods such as tracking the number of revolutions of its wheels; this corresponds to the idiothetic source and can give the absolute position of the robot, but it is subject to cumulative error which can grow quickly.
The allothetic source corresponds to the sensors of the robot, like a camera, a microphone, laser, lidar or sonar. The problem here is "perceptual aliasing". This means that two different places can be perceived at the same. For example, in a building, it is nearly impossible to determine a location solely with the visual information, because all the corridors may look the same.
The internal representation of the map can be "metric" or "topological":
- The metric framework is the most common for humans and considers a two-dimensional space in which it places the objects. The objects are placed with precise coordinates. This representation is very useful, but is sensitive to noise and it is difficult to calculate the distances precisely.
- The topological framework only considers places and relations between them. Often, the distances between places are stored. The map is then a graph, in which the nodes corresponds to places and arcs correspond to the paths.
Many techniques use probabilistic representations of the map, in order to handle uncertainty.
There are three main methods of map representations, i.e., free space maps, object maps, and composite maps. These employ the notion of a grid, but permit the resolution of the grid to vary so that it can become finer where more accuracy is needed and more coarse where the map is uniform.
Map-learning cannot be separated from the localization process, and a difficulty arises when errors in localization are incorporated into the map. This problem is commonly referred to as Simultaneous localization and mapping (SLAM).
An important additional problem is to determine whether the robot is in a part of environment already stored or never visited. One way to solve this problem is by using electric beacons.
Path planning is an important issue as it allows a robot to get from point A to point B. Path planning algorithms are measured by their computational complexity. The feasibility of real-time motion planning is dependent on the accuracy of the map (or floorplan), on robot localization and on the number of obstacles. Topologically, the problem of path planning is related to the shortest path problem problem of finding a route between two nodes in a graph.
Outdoor robots can use GPS in a similar way to automotive navigation systems. Alternative systems can be used with floor plan instead of maps for indoor robots, combined with localization wireless hardware. Electric beacons also have been proposed for cheap robot navigational systems.
- Automotive navigation system
- Domestic robot
- AVM Navigator
- Electric beacon
- Map database management
- Maze Simulator
- Robotics suite
- Occupancy grid
- Simultaneous localization and mapping (SLAM)
- Multi Autonomous Ground-robotic International Challenge: A challenge requiring multiple vehicles to collaboratively map a large dynamic urban environment