  This paper presents a state-of-the-art filter that reduces the complexity in
object detection, tracking and mapping applications. Existing edge detection
and tracking methods are proposed to create suitable autonomy for mobile
robots, however, many of them face overconfidence and large computations at the
entrance to scenarios with an immense number of landmarks. The method in this
work, the Line-Circle-Square (LCS) filter, claims that mobile robots without a
large database for object recognition and highly advanced prediction methods
can deal with incoming objects that the camera captures in real-time. The
proposed filter applies detection, tracking and learning to each defined expert
to extract higher level information for judging scenes without
over-calculation. The interactive learning feed between each expert increases
the consistency of detected landmarks that works against overwhelming detected
features in crowded scenes. Our experts are dependent on trust factors'
covariance under the geometric definitions to ignore, emerge and compare
detected landmarks. The experiment validates the effectiveness of the proposed
filter in terms of detection precision and resource usage in both experimental
and real-world scenarios.
