The problem of Robotic mapping is related to cartography. The goal is for an autonomous robot to be able to construct a map and to localize itself in it.

Actually, Todd et al (1994) have shown that evolutionary shaped blind action may suffice keep alive. For some real insects for example, the environment is not interpreted as a map, and they survive only with a triggered response.

But a slightly more elaborated navigation strategy dramatically enhances the capabilities of the robot. Cognitive map (Tolman 1948) are better because they enable planning capacities, and use of current perceptions, memorized events, and expected consequences.

A good algorithm in robotic mapping may combine the information from the past, the present and the future (Trullier et al. 1997).

The problem can be decomposed in three processes (Levitt and Lawton 1990 ; Balakrishnan et al. 1999) : map learning, localisation, path-planning.

Contents

1 Available information 2 Map representation 3 Map learning 4 Path planning

Available information

The robot has two sources of information : the idiothetic and the allothetic sources.

When it moves, the robot integrates his position by counting the number of wheel turns it has done. This corresponds to the idiothetic source. It can give the absolute position of the robot. But it is subject of cumulative error which can grow fast.

The allothetic source corresponds to the sensors of the robot, like a camera, a microphone, laser, sonar, ... The problem is the "perceptual aliasing". This means that two differents places can be perceived the same. For example, in a building, you can't know where you are only with the visual information, because all the corridors look like the same.

Map representation

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 calcule precisely the distances.

The topological framework only considers places and relations between them. Most of times, the distances between places are stored. The map is then a graph (mathematics), in which the nodes corresponds to places, an arc to the paths.

Almost all state-of-art solutions use probabilistic representation of the map, in order to correctly handle uncertainty.

Map learning

Map-learning can't be separated from the localization process so it is difficult because errors in localization are incorporated into the map. This problem is related as Simultaneous localization and mapping (SLAM).

An important additional problem is to determine whether the robot is in a part of environnement already stored or never visited.

Path planning

The path planning problem is not an important issue if the map and localization are accurate. For the metric representation of the map, the robot can find short cuts in the map. For the topological representation, the problem of path planning is a classical problem of finding the shortest path between two nodes in a graph (mathematics).