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Toward Robot Perception through Omnidirectional Vision 255

Fig. 15. Experiment combining visual path following for door traversal and topo-
logical navigation for corridor following

During this backward trajectory we use the same image eigenspaces as
were utilised during the forward motion by simply rotating, in real-time,
the acquired omnidirectional images by 180 . Alternatively, we could use
◦
the image’s power spectrum or the Zero Phase Representation [69]. Finally,
once the robot is approximately located at the lab entrance, control is passed
to the Visual Path Following module. Immediately it locates the visual land-
marks and drives the robot through the door. It follows a pre-specified path
until the final goal position, well inside the lab, is reached. Figure 15 shows
an image sequence to relate the robot’s motion during this experiment.
In Fig. 16(a) we used odometric readings from the best experiment to plot
the robot trajectory. When returning to the laboratory, the uncertainty in
odometry was approximately 0.5m. Thus, door traversal would not be possi-
ble without the use of visual control. Figure 16(b), shows the actual robot
trajectory, after using ground truth measurements to correct the odometric
estimates. The mission was successfully accomplished.
This integrated experiment shows that omnidirectional images are advan-
tageous for navigation and support different representations suitable both for
Topological Maps, when navigating between distant environmental points,
and Visual Path Following for accurate path traversal. Additionally, we have
described how they can help in coping with occlusions, and with methods of
achieving robustness against illumination changes.

4 Complementing Human and Robot Perceptions
for HR Interaction

Each omnidirectional image provides a rich description and understanding
of the scene. Visualization methods based on panoramic or bird’s eye views
provide a simple and effective way to control the robot. For instance, the