143 research outputs found
CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration
We present CLIPPER+, an algorithm for finding maximal cliques in unweighted
graphs for outlier-robust global registration. The registration problem can be
formulated as a graph and solved by finding its maximum clique. This
formulation leads to extreme robustness to outliers; however, finding the
maximum clique is an NP-hard problem, and therefore approximation is required
in practice for large-size problems. The performance of an approximation
algorithm is evaluated by its computational complexity (the lower the runtime,
the better) and solution accuracy (how close the solution is to the maximum
clique). Accordingly, the main contribution of CLIPPER+ is outperforming the
state-of-the-art in accuracy while maintaining a relatively low runtime.
CLIPPER+ builds on prior work (CLIPPER [1] and PMC [2]) and prunes the graph by
removing vertices that have a small core number and cannot be a part of the
maximum clique. This will result in a smaller graph, on which the maximum
clique can be estimated considerably faster. We evaluate the performance of
CLIPPER+ on standard graph benchmarks, as well as synthetic and real-world
point cloud registration problems. These evaluations demonstrate that CLIPPER+
has the highest accuracy and can register point clouds in scenarios where over
of associations are outliers. Our code and evaluation benchmarks are
released at https://github.com/ariarobotics/clipperp
Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM
Global registration is a fundamental task that estimates the relative pose
between two viewpoints of 3D point clouds. However, there are two issues that
degrade the performance of global registration in LiDAR SLAM: one is the
sparsity issue and the other is degeneracy. The sparsity issue is caused by the
sparse characteristics of the 3D point cloud measurements in a mechanically
spinning LiDAR sensor. The degeneracy issue sometimes occurs because the
outlier-rejection methods reject too many correspondences, leaving less than
three inliers. These two issues have become more severe as the pose discrepancy
between the two viewpoints of 3D point clouds becomes greater. To tackle these
problems, we propose a robust global registration framework, called
\textit{Quatro++}. Extending our previous work that solely focused on the
global registration itself, we address the robust global registration in terms
of the loop closing in LiDAR SLAM. To this end, ground segmentation is
exploited to achieve robust global registration. Through the experiments, we
demonstrate that our proposed method shows a higher success rate than the
state-of-the-art global registration methods, overcoming the sparsity and
degeneracy issues. In addition, we show that ground segmentation significantly
helps to increase the success rate for the ground vehicles. Finally, we apply
our proposed method to the loop closing module in LiDAR SLAM and confirm that
the quality of the loop constraints is improved, showing more precise mapping
results. Therefore, the experimental evidence corroborated the suitability of
our method as an initial alignment in the loop closing. Our code is available
at https://quatro-plusplus.github.io.Comment: 26 pages, 23 figure
Point Pair Feature based Object Detection for Random Bin Picking
Point pair features are a popular representation for free form 3D object
detection and pose estimation. In this paper, their performance in an
industrial random bin picking context is investigated. A new method to generate
representative synthetic datasets is proposed. This allows to investigate the
influence of a high degree of clutter and the presence of self similar
features, which are typical to our application. We provide an overview of
solutions proposed in literature and discuss their strengths and weaknesses. A
simple heuristic method to drastically reduce the computational complexity is
introduced, which results in improved robustness, speed and accuracy compared
to the naive approach
A Solution to The Similarity Registration Problem of Volumetric Shapes
This paper provides a novel solution to the volumetric similarity registration problem usually encountered in statistical study of shapes and shape-based image segmentation. Shapes are implicitly representedby characteristic functions (CFs). By mapping shapes to a spherical coordinate system, shapes to be registered are projected to unit spheres and thus, rotation and scale parameters can be conveniently calculated.Translation parameter is computed using standard phase correlation technique. The method goes through intensive tests and is shown to be fast, robust to noise and initial poses, and suitable for a variety of similarity registration problems including shapes with complex structures and various topologies
Learning to Place New Objects
The ability to place objects in the environment is an important skill for a
personal robot. An object should not only be placed stably, but should also be
placed in its preferred location/orientation. For instance, a plate is
preferred to be inserted vertically into the slot of a dish-rack as compared to
be placed horizontally in it. Unstructured environments such as homes have a
large variety of object types as well as of placing areas. Therefore our
algorithms should be able to handle placing new object types and new placing
areas. These reasons make placing a challenging manipulation task. In this
work, we propose a supervised learning algorithm for finding good placements
given the point-clouds of the object and the placing area. It learns to combine
the features that capture support, stability and preferred placements using a
shared sparsity structure in the parameters. Even when neither the object nor
the placing area is seen previously in the training set, our algorithm predicts
good placements. In extensive experiments, our method enables the robot to
stably place several new objects in several new placing areas with 98%
success-rate; and it placed the objects in their preferred placements in 92% of
the cases
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