  This paper presents a state-of-the-art approach in object detection for being
applied in future SLAM problems. Although, many SLAM methods are proposed to
create suitable autonomy for mobile robots namely ground vehicles, they still
face overconfidence and large computations during entrance to immense spaces
with many landmarks. In particular, they suffer from impractical applications
via sole reliance on the limited sensors like camera. Proposed method claims
that unmanned ground vehicles without having huge amount of database for object
definition and highly advance prediction parameters can deal with incoming
objects during straight motion of camera in real-time. Line-Circle (LC) filter
tries to apply detection, tracking and learning to each defined experts to
obtain more information for judging scene without over-calculation. In this
filter, circle expert let us summarize edges in groups. The Interactive
feedback learning between each expert creates minimal error that fights against
overwhelming landmark signs in crowded scenes without mapping. Our experts
basically are dependent on trust factors' covariance with geometric definitions
to ignore, emerge and compare detected landmarks. The experiment for validating
the model is taken place utilizing a camera beside an IMU sensor for location
estimation.
