Computational Intelligence - November 2013 - 59

results from rear and front views, 3D
model-based data matching plays an
important role in the fusion. Fast 3D
model-based searching within a large
GIDS database is another significant challenge, in which we have considerable
experience and knowledge.
B. Learning-Enabled Object
Detection (LOD)

LOD provides the ability to perceive surrounding objects by extracting meaningful information from sensor data, and is
the key to autonomous systems. This is
especially true for localizing and navigating unmanned vehicles; such as a rover in
a dynamic environment. Rock-like
objects can be classified as hazards that
could potentially obstruct the rover's path;
landmarks that can be used as reference
points for localization and building maps,
or ROI, or scientifically important rocks
on Mars. Objects can be retrieved by processing various types of sensor data but we
focus here on images from PanCam. Current vision-based methods of detecting
and classifying objects are based on the
object's geometric features and appearance. The dominant approaches for realtime processing of objects use holistic
generative and discriminative models [24].
Generative holistic models are suitable for
recognizing objects with relatively uniform geometric properties (e.g. pipes,
faces) whereas discriminative models
work best with previous knowledge of
the appearance of the object that is stored
in a template during training. Naturally,
discriminative models run faster but are
restricted by the available templates. Our
object detection is achieved by processing
and fusing data from multiple sensors
including images captured by PanCam.
C. Self-Learning Agent (SLA)

SLA is capable of inferring objects from
partial knowledge of the detected features
by utilizing a multitude of data processing
techniques. The SLA employs a hierarchical approach where class descriptions are
learnt based on the complexity of the classification method as described in Figure 15.
A template of object classes is stored and
updated in the Environment Model
Library (EML) upon successful detection

Figure 11 Shows investigation (6): Sol 22 WAC panorama.

4 Sub-Framed 256 # 256 ROI
from the Original Images in
Figure 8(b), (c), (f) and (g).

Super-Resolution (1536 # 1536) Image of
Sub-Framed ROI of Mount Everest North
Face with 2:1 Lossless Compression.

Histogram of Super-Resolution
Image with 2:1 Compression.

0

255

Figure 12 Shows sub-framed ROI, compression and super-resolution.

PanCam_Takes_Image
Iterations

Subframing

Rover_PTU

Compression

Thumbnail

ExoMars Rover: PanCam
Uplink

Downlink
Earth
Decompression

Select_ROI
ROCC
Super_Resolution

High_Resolution_Image

Figure 13 The use case diagram.

November 2013 | Ieee ComputatIoNal INtellIgeNCe magazINe

59



Table of Contents for the Digital Edition of Computational Intelligence - November 2013

Computational Intelligence - November 2013 - Cover1
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