2 research outputs found
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery
We present an unsupervised framework for simultaneous appearance-based object
discovery, detection, tracking and reconstruction using RGBD cameras and a
robot manipulator. The system performs dense 3D simultaneous localization and
mapping concurrently with unsupervised object discovery. Putative objects that
are spatially and visually coherent are manipulated by the robot to gain
additional motion-cues. The robot uses appearance alone, followed by structure
and motion cues, to jointly discover, verify, learn and improve models of
objects. Induced motion segmentation reinforces learned models which are
represented implicitly as 2D and 3D level sets to capture both shape and
appearance. We compare three different approaches for appearance-based object
discovery and find that a novel form of spatio-temporal super-pixels gives the
highest quality candidate object models in terms of precision and recall. Live
experiments with a Baxter robot demonstrate a holistic pipeline capable of
automatic discovery, verification, detection, tracking and reconstruction of
unknown objects
Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization
Object shape provides important information for robotic manipulation; for
instance, selecting an effective grasp depends on both the global and local
shape of the object of interest, while reaching into clutter requires accurate
surface geometry to avoid unintended contact with the environment. Model-based
3D object manipulation is a widely studied problem; however, obtaining the
accurate 3D object models for multiple objects often requires tedious work. In
this letter, we exploit Gaussian process implicit surfaces (GPIS) extracted
from RGB-D sensor data to grasp an unknown object. We propose a
reconstruction-aware trajectory optimization that makes use of the extracted
GPIS model plan a motion to improve the ability to estimate the object's 3D
geometry, while performing a pick-and-place action. We present a probabilistic
approach for a robot to autonomously learn and track the object, while achieve
the manipulation task.
We use a sampling-based trajectory generation method to explore the unseen
parts of the object using the estimated conditional entropy of the GPIS model.
We validate our method with physical robot experiments across eleven different
objects of varying shape from the YCB object dataset. Our experiments show that
our reconstruction-aware trajectory optimization provides higher-quality 3D
object reconstruction when compared with directly solving the manipulation task
or using a heuristic to view unseen portions of the object