Tracking-based interactive segmentation of textureless objects

This paper describes a textureless object segmentation approach for autonomous service robots acting in human living environments. The proposed system allows a robot to effectively segment textureless objects in cluttered scenes by leveraging its manipulation capabilities. In our pipeline, the cluttered scenes are first statically segmented using state-of-the-art classification algorithm and then the interactive segmentation is deployed in order to resolve this possibly ambiguous static segmentation. In the second step the RGBD (RGB + Depth) sparse features, estimated on the RGBD point cloud from the Kinect sensor, are extracted and tracked while motion is induced into a scene. Using the resulting feature poses, the features are then assigned to their corresponding objects by means of a graph-based clustering algorithm. In the final step, we reconstruct the dense models of the objects from the previously clustered sparse RGBD features. We evaluated the approach on a set of scenes which consist of various textureless flat (e. g. box-like) and round (e. g. cylinder-like) objects and the combinations thereof.

Authors

• Karol Hausman
• Ferenc Balint-Benczedi
• Dejan Pangercic
• Zoltán-Csaba Márton
• Ryohei Ueda
• Kei Okada
• Michael Beetz

Keywords

• Feature extraction
• Motion segmentation
• Three-dimensional displays
• Clustering algorithms
• Tracking
• Computational modeling
• Interactive Segmentation
• Texture-less Objects
• Clustering Algorithm
• Point Cloud
• Density Model
• Object Segmentation
• Sparse Feature
• Graph-based Clustering
• Graph-based Algorithm
• Service Robots
• Kinect Sensor
• Set Of Scenes
• Point Estimates
• Scope Of This Paper
• Poisson Distribution
• Similar Shape
• Contact Point
• Kullback-Leibler
• Object Features
• Number Of Objects
• End-effector
• Object Parts
• Hash Function
• Dense Reconstruction
• Solid Contact
• Trajectory Clustering
• Normal Space
• Input Point Cloud
• Object Size
• Arm Motion