IEEE Robotics & Automation Magazine - June 2014 - 75

Table 2. Object estimation scenarios.
Setup: A robot (blue rectangle) tracks moving objects (red rectangles) using a sensor with limited surveillance area
(yellow circle segment).
Wide: If the FOV is wide, the sensor can see to the sides as
well, and it becomes possible to detect multiple objects.

Low

Single-point object: A single
object that generates at most
a single detection. This is sufficient for certain applications,
e.g., distance keeping.

Multiple-point objects: Multiple objects may be detected,
and any object within the
sensor's range may generate
at most a single detection.

High

Resolution (relative object size)

FOV
Narrow: With a narrow FOV, the robot can essentially only
see straight ahead and only detect a single object.

Single extended object: A
single object that may generate multiple detections. It is
possible to estimate the width
of the leading vehicle in addition to the distance.

Multiple extended objects:
Multiple objects may be
detected, and any object
within the sensor's range may
generate multiple detections.

landmarks that are extended objects. A list of concepts that
are central to multiple-extended-object estimation is given
in Table 1.
Background and Motivation
To estimate the states of these multiple objects, the robot
must be equipped with one or several exteroceptive sensors
that allow it to perceive the world, e.g., laser range sensors,
radar sensors, or cameras. To keep things simple, in this
article we assume that a single sensor is used and we
assume that this sensor is of a type similar to laser range
and radar sensors. Note, however, that the material presented here can be easily generalized to other sensor types,
such as cameras.
The sensor has a field of view (FOV) and a range, that
together define the sensor's surveillance area. Both the FOV
and range can be described further in terms of their respective resolution. With time, technology development is moving
toward an increase in all these properties, i.e., wider FOV, longer range, and higher resolution.
Consider the FOV and its resolution: Depending on
whether the FOV is narrow or wide and whether the resolution is low or high (relative to the size of the objects), four different kinds of object estimation problems arise, as shown in
Table 2. At this point, it becomes necessary to distinguish
between objects that may cause only a single detection each
and objects that may cause multiple detections each. These
types of objects are called point objects and extended objects,
respectively (see Table 1).
In the extended-object case, depending on the type of
sensor used, the multiple detections will either be spread
across the object's surface (e.g., when an airborne radar is
used to track ground objects), or be spread along the edge of
the object's shape (e.g., when a laser is used to track vehicles
and persons). In extended-object estimation, it is generally
not of interest to estimate the locations of the points that
cause the detections because these points usually change

quickly with varying sensor-to-object geometry. Instead, it is
the principal extended object as a whole, i.e., its position,
shape, size, and orientation, that is of interest. Having estimates of the objects' extensions in addition to estimates of
the kinematic states is useful for different robotics applications, such as the following:
● Path-planning and collision avoidance. When a robot
moves, it must plan its path such that it only traverses
open area because trivially it cannot go from one room to
another by going through a wall. Furthermore, in a
crowded scene, it must pass by both stationary and moving objects without hitting them. To succeed in both of
these tasks, it is necessary to know not only the location of
the objects but also their spatial extent.
● Classification of objects into different object types, e.g., car,
bicycle, or pedestrian in an urban environment. This is
needed, e.g., for the robot to be able to interact with the
objects in the correct manner.
Let us return to the sensor's properties. The FOV and the
range of the sensor determine the sensor's surveillance area.
Typically both the FOV and range are limited and thus the
surveillance area is limited. Trivially, it cannot be known a priori how many objects there are inside the sensor's surveillance
area, and during the course of operation, objects might exit
the surveillance area and new objects might enter it. Objects
that are inside the surveillance area may also be invisible to
the sensor due to occlusion from other objects, and false
detections of nonexistent objects may complicate things further. Thus, the number of visible objects is both time varying
and unknown.
An example of the multiple-extended-object scenario is
given in Figure 2. The detections display a large degree of
structure, especially the L-shaped cluster caused by the car,
and it is therefore suitable to estimate the shape and size of
the objects in addition to their positions. The sensor's
FOV is 180° with a resolution of 0.5° and the maximum
range is 13 m, which gives a semicircular surveillance area.
June 2014

IEEE ROBOTICS & AUTOMATION MAGAZINE

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2014