For autonomous vehicles and robots a fast, complete, and reliable acquisition of the environment is crucial for
almost every task they perform. To fulfil this, optical sensors with different spectral sensibility are one of the
most important sensors as they provide very rich information about the scene. Regarding outdoor environments,
the contained dynamics are very high which arise on the one hand from object movements and self motion and
on the other hand from changing lighting conditions due to varying weather conditions. These high dynamics
hinder a reliable scene acquisition using conventional optical sensors as they only offer a limited sampling rate,
resolution, and dynamic range. To overcome these limitations without using specialized hardware we propose an
assembly of several cameras and beam-splitters which we call a multimodal-camera. The cameras take images
from the same scene from slightly different viewpoints and with diverse parameters like exposure, or shutter time
which are all adjustable. By combing these images and applying techniques from computer graphics, we are able
to create an output by computation that covers the scene's high dynamics and can be used for a reliable scene
analysis.
In this work, we use the principles of Swarm Intelligence to establish a novel algorithm for detecting and describing
straight edges in images. The algorithm uses a set of individual mobile agents with limited cognitive possibilities.
Using their memory and communication abilities, the agents can establish fast and robust solutions. The agents
initially move randomly in a two dimensional space defined by an arbitrary input image or image sequence.
In every time step, each agent calculates the derivative values in x and y direction at its current position and
thresholds these values subsequently. If an agent discovers an edge or respectively a straight edge, it follows this
straight edge and stores its start point. When it reaches the straight edge's end, it marks its last position as its
stop point. As a kind of indirect communication between the agents, each of them leaves important information
at each new position discovered. Thus each agent can benefit from the calculations any other agent has done
before, which speeds up the algorithm. This new approach is a fast alternative to classical line finding operation
like e.g. the Hough Transform.
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