17 research outputs found
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates
We propose a robust approach for the registration of two sets of 3D points in
the presence of a large amount of outliers. Our first contribution is to
reformulate the registration problem using a Truncated Least Squares (TLS) cost
that makes the estimation insensitive to a large fraction of spurious
point-to-point correspondences. The second contribution is a general framework
to decouple rotation, translation, and scale estimation, which allows solving
in cascade for the three transformations. Since each subproblem (scale,
rotation, and translation estimation) is still non-convex and combinatorial in
nature, out third contribution is to show that (i) TLS scale and
(component-wise) translation estimation can be solved exactly and in polynomial
time via an adaptive voting scheme, (ii) TLS rotation estimation can be relaxed
to a semidefinite program and the relaxation is tight in practice, even in the
presence of an extreme amount of outliers. We validate the proposed algorithm,
named TEASER (Truncated least squares Estimation And SEmidefinite Relaxation),
in standard registration benchmarks showing that the algorithm outperforms
RANSAC and robust local optimization techniques, and favorably compares with
Branch-and-Bound methods, while being a polynomial-time algorithm. TEASER can
tolerate up to 99% outliers and returns highly-accurate solutions.Comment: 18 pages, Accepted for publication in Robotics: Science and Systems,
201
A Dynamical Perspective on Point Cloud Registration
We provide a dynamical perspective on the classical problem of 3D point cloud
registration with correspondences. A point cloud is considered as a rigid body
consisting of particles. The problem of registering two point clouds is
formulated as a dynamical system, where the dynamic model point cloud
translates and rotates in a viscous environment towards the static scene point
cloud, under forces and torques induced by virtual springs placed between each
pair of corresponding points. We first show that the potential energy of the
system recovers the objective function of the maximum likelihood estimation. We
then adopt Lyapunov analysis, particularly the invariant set theorem, to
analyze the rigid body dynamics and show that the system globally
asymptotically tends towards the set of equilibrium points, where the globally
optimal registration solution lies in. We conjecture that, besides the globally
optimal equilibrium point, the system has either three or infinite "spurious"
equilibrium points, and these spurious equilibria are all locally unstable. The
case of three spurious equilibria corresponds to generic shape of the point
cloud, while the case of infinite spurious equilibria happens when the point
cloud exhibits symmetry. Therefore, simulating the dynamics with random
perturbations guarantees to obtain the globally optimal registration solution.
Numerical experiments support our analysis and conjecture.Comment: Preliminary results, 10 pages, 4 figure
OLAE-ICP: Robust and fast alignment of geometric features with the optimal linear attitude estimator
The problems of point-cloud registration and attitude estimation from vector
observations (Wahba's problem) have widespread applications in computer vision
and mobile robotics. This work introduces a simple approach for integrating
sets of geometric feature observations (points, lines, and planes) in such a
way that any solution to either point-cloud registration or to Wahba's problem
can be used to find the SE(3) transformation between the two sets that
minimizes the corresponding cost function. We compare the performance of three
solutions: classic Horn's optimal quaternion method, Optimal Linear Attitude
Estimator (OLAE) that efficiently recovers the optimal Gibbs-Rodrigues vector
solving a small linear system, and an iterative non-linear Gauss-Newton solver.
Special care is given to explain how to overcome the Gibbs vector singularity
for OLAE by using the method of sequential rotations. Gross outliers in
point-to-point correspondences can be discarded by means of detecting
transformation scale mismatches. The approach also allows the introduction of
per-primitive relative weights, including an optional robust loss function that
is applicable only if an initial guess for the solution is known in advance.
Experiments are presented to evaluate how the three solutions tolerate noise in
the input data for different kinds of geometric primitives. Finally,
experiments with real datasets validate the suitability of the optimal
alignment algorithm as the core of an Iterative Closest Point/Primitive (ICP)
algorithm. An open-source implementation of all the described algorithms is
provided in https://github.com/MOLAorg/mp2p_icpComment: Supplementary material to conference paper at "Robotics: Science and
Systems" (RSS 2019
A BRIEF OVERVIEW OF THE CURRENT STATE, CHALLENGING ISSUES AND FUTURE DIRECTIONS OF POINT CLOUD REGISTRATION
Point cloud registration is the process of transforming multiple point clouds obtained at different locations of the same scene into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to the typical target-based registration method, there are various registration methods that are based on using only the point cloud data captured (i.e. cloud-to-cloud methods). Until recently, cloud-to-cloud registration methods have generally adopted a coarse-to-fine optimisation process. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of applying deep learning approaches to imagery data, numerous attempts at applying such approaches to point cloud datasets have received much attention. This study reviews and comment on recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
In this paper, we propose a novel learning-based pipeline for partially
overlapping 3D point cloud registration. The proposed model includes an
iterative distance-aware similarity matrix convolution module to incorporate
information from both the feature and Euclidean space into the pairwise point
matching process. These convolution layers learn to match points based on joint
information of the entire geometric features and Euclidean offset for each
point pair, overcoming the disadvantage of matching by simply taking the inner
product of feature vectors. Furthermore, a two-stage learnable point
elimination technique is presented to improve computational efficiency and
reduce false positive correspondence pairs. A novel mutual-supervision loss is
proposed to train the model without extra annotations of keypoints. The
pipeline can be easily integrated with both traditional (e.g. FPFH) and
learning-based features. Experiments on partially overlapping and noisy point
cloud registration show that our method outperforms the current
state-of-the-art, while being more computationally efficient. Code is publicly
available at https://github.com/jiahaowork/idam
PRNet: Self-Supervised Learning for Partial-to-Partial Registration
We present a simple, flexible, and general framework titled Partial
Registration Network (PRNet), for partial-to-partial point cloud registration.
Inspired by recently-proposed learning-based methods for registration, we use
deep networks to tackle non-convexity of the alignment and partial
correspondence problems. While previous learning-based methods assume the
entire shape is visible, PRNet is suitable for partial-to-partial registration,
outperforming PointNetLK, DCP, and non-learning methods on synthetic data.
PRNet is self-supervised, jointly learning an appropriate geometric
representation, a keypoint detector that finds points in common between partial
views, and keypoint-to-keypoint correspondences. We show PRNet predicts
keypoints and correspondences consistently across views and objects.
Furthermore, the learned representation is transferable to classification.Comment: NeurIPS 201
Learning to Communicate and Correct Pose Errors
Learned communication makes multi-agent systems more effective by aggregating
distributed information. However, it also exposes individual agents to the
threat of erroneous messages they might receive. In this paper, we study the
setting proposed in V2VNet, where nearby self-driving vehicles jointly perform
object detection and motion forecasting in a cooperative manner. Despite a huge
performance boost when the agents solve the task together, the gain is quickly
diminished in the presence of pose noise since the communication relies on
spatial transformations. Hence, we propose a novel neural reasoning framework
that learns to communicate, to estimate potential errors, and finally, to reach
a consensus about those errors. Experiments confirm that our proposed framework
significantly improves the robustness of multi-agent self-driving perception
and motion forecasting systems under realistic and severe localization noise.Comment: Conference on Robot Learning (CoRL) 2020. 16 pages, 7 figure
CvxPnPL: A Unified Convex Solution to the Absolute Pose Estimation Problem from Point and Line Correspondences
We present a new convex method to estimate 3D pose from mixed combinations of
2D-3D point and line correspondences, the Perspective-n-Points-and-Lines
problem (PnPL). We merge the contributions of each point and line into a
unified Quadratic Constrained Quadratic Problem (QCQP) and then relax it into a
Semi Definite Program (SDP) through Shor's relaxation. This makes it possible
to gracefully handle mixed configurations of points and lines. Furthermore, the
proposed relaxation allows us to recover a finite number of solutions under
ambiguous configurations. In such cases, the 3D pose candidates are found by
further enforcing geometric constraints on the solution space and then
retrieving such poses from the intersections of multiple quadrics. Experiments
provide results in line with the best performing state of the art methods while
providing the flexibility of solving for an arbitrary number of points and
lines.Comment: Main paper and supplemental material included. References added and
minor change to fig
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Point clouds have attracted increasing attention as a natural representation
of 3D shapes. Significant progress has been made in developing methods for
point cloud analysis, which often requires costly human annotation as
supervision in practice. To address this issue, we propose a novel
self-contrastive learning for self-supervised point cloud representation
learning, aiming to capture both local geometric patterns and nonlocal semantic
primitives based on the nonlocal self-similarity of point clouds. The
contributions are two-fold: on the one hand, instead of contrasting among
different point clouds as commonly employed in contrastive learning, we exploit
self-similar point cloud patches within a single point cloud as positive
samples and otherwise negative ones to facilitate the task of contrastive
learning. Such self-contrastive learning is well aligned with the emerging
paradigm of self-supervised learning for point cloud analysis. On the other
hand, we actively learn hard negative samples that are close to positive
samples in the representation space for discriminative feature learning, which
are sampled conditional on each anchor patch leveraging on the degree of
self-similarity. Experimental results show that the proposed method achieves
state-of-the-art performance on widely used benchmark datasets for
self-supervised point cloud segmentation and transfer learning for
classification.Comment: Accepted to ACM MM 202
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
Many estimation problems in robotics, computer vision, and learning require
estimating unknown quantities in the face of outliers. Outliers are typically
the result of incorrect data association or feature matching, and it is common
to have problems where more than 90% of the measurements used for estimation
are outliers. While current approaches for robust estimation are able to deal
with moderate amounts of outliers, they fail to produce accurate estimates in
the presence of many outliers. This paper develops an approach to prune
outliers. First, we develop a theory of invariance that allows us to quickly
check if a subset of measurements are mutually compatible without explicitly
solving the estimation problem. Second, we develop a graph-theoretic framework,
where measurements are modeled as vertices and mutual compatibility is captured
by edges. We generalize existing results showing that the inliers form a clique
in this graph and typically belong to the maximum clique. We also show that in
practice the maximum k-core of the compatibility graph provides an
approximation of the maximum clique, while being faster to compute in large
problems. These two contributions leads to ROBIN, our approach to Reject
Outliers Based on INvariants, which allows us to quickly prune outliers in
generic estimation problems. We demonstrate ROBIN in four geometric perception
problems and show it boosts robustness of existing solvers while running in
milliseconds in large problems