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Where am I? Sensors and Methods for Mobile Robot Positioning

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CHAPTER 7<br />

LANDMARK NAVIGATION<br />

L<strong>and</strong>marks are distinct features that a robot can recognize from its sensory input. L<strong>and</strong>marks can<br />

be geometric shapes (e.g., rectangles, lines, circles), <strong>and</strong> they may include additional in<strong>for</strong>mation<br />

(e.g., in the <strong>for</strong>m of bar-codes). In general, l<strong>and</strong>marks have a fixed <strong>and</strong> known position, relative to<br />

which a robot can localize itself. L<strong>and</strong>marks are carefully chosen to be easy to identify; <strong>for</strong> ex<strong>am</strong>ple,<br />

there must be sufficient contrast to the background. Be<strong>for</strong>e a robot can use l<strong>and</strong>marks <strong>for</strong> navigation,<br />

the characteristics of the l<strong>and</strong>marks must be known <strong>and</strong> stored in the robot's memory. The main task<br />

in localization is then to recognize the l<strong>and</strong>marks reliably <strong>and</strong> to calculate the robot's position.<br />

In order to simplify the problem of l<strong>and</strong>mark acquisition it is often assumed that the current robot<br />

position <strong>and</strong> orientation are known approximately, so that the robot only needs to look <strong>for</strong> l<strong>and</strong>marks<br />

in a limited area. For this reason good odometry accuracy is a prerequisite <strong>for</strong> successful l<strong>and</strong>mark<br />

detection.<br />

The general procedure <strong>for</strong> per<strong>for</strong>ming l<strong>and</strong>mark-based positioning is shown in Figure 7.1. Some<br />

approaches fall between l<strong>and</strong>mark <strong>and</strong> map-based positioning (see Chap. 8). They use sensors to<br />

sense the environment <strong>and</strong> then extract distinct structures that serve as l<strong>and</strong>marks <strong>for</strong> navigation in<br />

the future. These approaches will be discussed in the chapter on map-based positioning techniques.<br />

Acquire sensory<br />

in<strong>for</strong>mation 1,2<br />

Notes:<br />

Detect <strong>and</strong><br />

4<br />

Calculate<br />

segment<br />

position 5,6<br />

l<strong>and</strong>marks 3,4<br />

Feng1pos.ds4; .wmf<br />

Figure 7.1: General procedure <strong>for</strong> l<strong>and</strong>mark-based positioning.<br />

Our discussion in this chapter addresses two types of l<strong>and</strong>marks: “artificial” <strong>and</strong> “natural.” It is<br />

important to bear in mind that “natural” l<strong>and</strong>marks work best in highly structured environments such<br />

as corridors, manufacturing floors, or hospitals. Indeed, one may argue that “natural” l<strong>and</strong>marks<br />

work best when they are actually man-made (as is the case in highly structured environments). For<br />

this reason, we shall define the terms “natural l<strong>and</strong>marks” <strong>and</strong> “artificial l<strong>and</strong>marks” as follows:<br />

natural l<strong>and</strong>marks are those objects or features that are already in the environment <strong>and</strong> have a<br />

function other than robot navigation; artificial l<strong>and</strong>marks are specially designed objects or markers<br />

that need to be placed in the environment with the sole purpose of enabling robot navigation.

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