10 research outputs found
Quantization, Calibration and Planning for Euclidean Motions in Robotic Systems
The properties of Euclidean motions are fundamental in all areas of robotics research. Throughout the past several decades, investigations on some low-level tasks like parameterizing specific movements and generating effective motion plans have fostered high-level operations in an autonomous robotic system. In typical applications, before executing robot motions, a proper quantization of basic motion primitives could simplify online computations; a precise calibration of sensor readings could elevate the accuracy of the system controls. Of particular importance in the whole autonomous robotic task, a safe and efficient motion planning framework would make the whole system operate in a well-organized and effective way. All these modules encourage huge amounts of efforts in solving various fundamental problems, such as the uniformity of quantization in non-Euclidean manifolds, the calibration errors on unknown rigid transformations due to the lack of data correspondence and noise, the narrow passage and the curse of dimensionality bottlenecks in developing motion planning algorithms, etc. Therefore, the goal of this dissertation is to tackle these challenges in the topics of quantization, calibration and planning for Euclidean motions
Robust and Accurate Superquadric Recovery: a Probabilistic Approach
Interpreting objects with basic geometric primitives has long been studied in
computer vision. Among geometric primitives, superquadrics are well known for
their ability to represent a wide range of shapes with few parameters. However,
as the first and foremost step, recovering superquadrics accurately and
robustly from 3D data still remains challenging. The existing methods are
subject to local optima and sensitive to noise and outliers in real-world
scenarios, resulting in frequent failure in capturing geometric shapes. In this
paper, we propose the first probabilistic method to recover superquadrics from
point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the
parametric surface of a superquadric, which explicitly models the generation of
outliers and noise. The superquadric recovery is formulated as a Maximum
Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation,
Maximization, and Switching (EMS), to solve this problem, where: (1) outliers
are predicted from the posterior perspective; (2) the superquadric parameter is
optimized by the trust-region reflective algorithm; and (3) local optima are
avoided by globally searching and switching among parameters encoding similar
superquadrics. We show that our method can be extended to the
multi-superquadrics recovery for complex objects. The proposed method
outperforms the state-of-the-art in terms of accuracy, efficiency, and
robustness on both synthetic and real-world datasets. The code is at
http://github.com/bmlklwx/EMS-superquadric_fitting.git.Comment: Accepted to CVPR202
Efficient Path Planning in Narrow Passages via Closed-Form Minkowski Operations
Path planning has long been one of the major research areas in robotics, with
PRM and RRT being two of the most effective classes of path planners. Though
generally very efficient, these sampling-based planners can become
computationally expensive in the important case of "narrow passages". This
paper develops a path planning paradigm specifically formulated for narrow
passage problems. The core is based on planning for rigid-body robots
encapsulated by unions of ellipsoids. The environmental features are enclosed
geometrically using convex differentiable surfaces (e.g., superquadrics). The
main benefit of doing this is that configuration-space obstacles can be
parameterized explicitly in closed form, thereby allowing prior knowledge to be
used to avoid sampling infeasible configurations. Then, by characterizing a
tight volume bound for multiple ellipsoids, robot transitions involving
rotations are guaranteed to be collision-free without traditional collision
detection. Furthermore, combining the stochastic sampling strategy, the
proposed planning framework can be extended to solving higher dimensional
problems in which the robot has a moving base and articulated appendages.
Benchmark results show that, remarkably, the proposed framework outperforms the
popular sampling-based planners in terms of computational time and success rate
in finding a path through narrow corridors and in higher dimensional
configuration spaces
Pose changes from a different point of view
For more than a century, rigid-body displacements have been viewed as affine transformations described as homogeneous transformation matrices wherein the linear part is a rotation matrix. In group-theoretic terms, this classical description makes rigid-body motions a semidirect product. The distinction between a rigid-body displacement of Euclidean space and a change in pose from one reference frame to another is usually not articulated well in the literature. Here, we show that, remarkably, when changes in pose are viewed from a space-fixed reference frame, the space of pose changes can be endowed with a direct product group structure, which is different from the semidirect product structure of the space of motions. We then show how this new perspective can be applied more naturally to problems such as monitoring the state of aerial vehicles from the ground, or the cameras in a humanoid robot observing pose changes of its hands.Division of Information and Intelligent Systems, National
Science Foundation (Grant No. IIS-1619050), Office of Naval Research Global (Grant No. N00014-17-1-2142)