Abstract
This paper describes a method for the detection
of textureless objects. Our target objects include furniture
and home appliances, which have no rich textural
features or characteristic shapes. Focusing on the ease of
application, we define a model that represents objects in
terms of three-dimensional edgels and surfaces. Object detection
is performed by superimposing input data on the
model. A two-stage algorithm is applied to bring out object
poses. Surfaces are used to extract candidates fromthe
input data, and edgels are then used to identify the pose
of a target object using two-dimensional template matching.
Experiments using four real furniture and home appliances
were performed to show the feasibility of the proposed
method.We suggest the possible applicability in occlusion
and clutter conditions.
Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking
