  3D object detection and pose estimation has been studied extensively in
recent decades for its potential applications in robotics. However, there still
remains challenges when we aim at detecting multiple objects while retaining
low false positive rate in cluttered environments. This paper proposes a robust
3D object detection and pose estimation pipeline based on RGB-D images, which
can detect multiple objects simultaneously while reducing false positives.
Detection begins with template matching and yields a set of template matches. A
clustering algorithm then groups templates of similar spatial location and
produces multiple-object hypotheses. A scoring function evaluates the
hypotheses using their associated templates and non-maximum suppression is
adopted to remove duplicate results based on the scores. Finally, a combination
of point cloud processing algorithms are used to compute objects' 3D poses.
Existing object hypotheses are verified by computing the overlap between model
and scene points. Experiments demonstrate that our approach provides
competitive results comparable to the state-of-the-arts and can be applied to
robot random bin-picking.
