  We propose a novel method utilizing an objectness score for maintaining the
locations and classes of objects detected from Mask R-CNN during mobile robot
navigation. The objectness score is defined to measure how well the detector
identifies the locations and classes of objects during navigation.
Specifically, it is designed to increase when there is sufficient distance
between a detected object and the camera. During the navigation process, we
transform the locations of objects in 3D world coordinates into 2D image
coordinates through an affine projection and decide whether to retain the
classes of detected objects using the objectness score. We conducted
experiments to determine how well the locations and classes of detected objects
are maintained at various angles and positions. Experimental results showed
that our approach is efficient and robust, regardless of changing angles and
distances.
