Planar Pose Research Articles

Enhancing Human-Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition.

With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase the task reliability by providing supplementary information. Motivated by this, we propose a multi-modal framework consisting of multiple independent modules. These modules take advantage of multiple sensors (e.g., image, sound, depth) and can be used separately or in combination for effective human-robot collaborative interaction. We identified and implemented four key components of an effective human robot collaborative setting, which included determining object location and pose, extracting intricate information from verbal instructions, resolving user(s) of interest (UOI), and gesture recognition and gaze estimation to facilitate the natural and intuitive interactions. The system uses a feature-detector-descriptor approach for object recognition and a homography-based technique for planar pose estimation and a deep multi-task learning model to extract intricate task parameters from verbal communication. The user of interest (UOI) is detected by estimating the facing state and active speakers. The framework also includes gesture detection and gaze estimation modules, which are combined with a verbal instruction component to form structured commands for robotic entities. Experiments were conducted to assess the performance of these interaction interfaces, and the results demonstrated the effectiveness of the approach.

An Efficient Visual SfM Framework Using Planar Markers

Structure-from-Motion (SfM) is a prevalent solution for vision measurement. Traditional SfM methods rely on sparse feature matching to establish visual associations between different views. However, both the incremental-based and global-based SfM show sensitiveness to outliers generated by existing hand-crafted features under challenging environments. Planar marker-assisted SfM is an alternative solution that employs artificial landmarks to help robust pose estimation. Nevertheless, using planar markers also exists the planar pose ambiguity problem, which means there may be more than one reasonable solution for pose estimation; it is a great challenge for camera pose estimation. This paper proposes an efficient planar marker-assisted global SfM method and resolves the planar pose ambiguity problem by examining the rotation consistency across multiple views. In particular, we construct two complementary graphs and present a graph filter method to filter out the views with pose ambiguity according to our designed rotation consistency checking. Afterward, we perform sparse feature matching following the proposed graph structure and make a pose-fixed bundle adjustment for high-quality 3D reconstruction. Extensive experiments have been conducted on synthetic and real datasets, and the results verify the effectiveness of the proposed method. Our method outperforms several state-of-the-art methods with encouraging efficiency, especially in challenging scenes.

Development and Control of a Bioinspired Robotic Remora for Hitchhiking

Remora, a bony fish, is well known for the remarkable hitchhiking behavior, by which it can transport itself over large distances without too much effort. In this article, a novel robotic remora with a stable adhesion system and its motion control methods are proposed for transferring the hitchhiking behavior to the engineered system. In terms of the mechatronic design, a robotic prototype with the uncoupled planar and vertical motion mechanisms is created to realize the precise and agile movement. Furthermore, a reliable adhesion system possessing low preload demand, considerable adhesive force ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim \text{250}\text{ N}$</tex-math></inline-formula> ), and reversible adhesion ability is developed, and then, the design principle of the adhesive disc is investigated. In the aspect of motion control, a mode-switch-based planar pose (position and orientation) control method and an antidisturbance depth control method are designed. The combination of these two controllers endows the robotic remora with the ability to precisely arrive at any position in a 3-D space, which paves the way for the hitchhiking task. Extensive experimental results are presented to support the performance benefits of the proposed robotic remora and control methods. Finally, the hitchhiking behavior is implemented successfully by robotic remora. To the best of our knowledge, this is the first engineered implementation of hitchhiking for a fin-actuated underwater robot. The results obtained will promote the design and control of the future underwater adhesion robot and offer valuable insights into the long-endurance robotic fish and mother–son multirobot system.

Object Rearrangement Through Planar Pushing: A Theoretical Analysis and Validation

In this article, we focus on rearranging an object by pushing it to any target planar pose. We identify the essential elements to guarantee that the target pose can be reached at an acceptable precision. We present a simple rearrangement algorithm that relies on only a few known straight-line pushes for some novel object and requires no analytical models, force sensors, or large training datasets. We derive the step upper bound, which relates the initial pose of the object, stopping criterion, and quality of the set of pushes, to facilitate the estimation of the maximum number of required steps without the need to perform a task. We experimentally verified the performance of our algorithm at different noise levels, stopping criteria, and task difficulties on datasets containing several types of objects. By applying combinations of only nine known pushes, our simple algorithm performed successfully in real-world experiments with challenging objects, including partially deformable objects that are difficult to model analytically; it achieved the precise stopping criterion (7.5 mm, 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> ) in various rearrangement tasks.

Accuracy evaluation of methods for pose estimation from fiducial markers

Estimating the pose from fiducial markers is a widely researched topic with practical importance for computer vision, robotics and photogrammetry. In this paper, we aim at quantifying the accuracy of pose estimation in real-world scenarios. More specifically, we investigate six different factors, which impact the accuracy of pose estimation, namely: number of points, depth offset, planar offset, manufacturing error, detection error, and constellation size. Their influence is quantified for four non-iterative pose estimation algorithms, employing direct linear transform, direct least squares, robust perspective n-point, and infinitesimal planar pose estimation, respectively. We present empirical results which are instructive for selecting a well-performing pose estimation method and rectifying the factors causing errors and degrading the rotational and translational accuracy of pose estimation.

Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions

Abstract We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaternions representing planar motions, the nonlinear structure of the SE(2) group is inherently considered. To solve the on-manifold least squares problem, a Riemannian Gauss– Newton method using the exponential retraction is applied. The proposed Riemannian pose graph optimizer (RPG-Opt) is further evaluated based on public planar pose graph data sets. Compared with state-of-the-art frameworks, the proposed method gives equivalent accuracy and better convergence robustness under large uncertainties of odometry measurements.

Planar Pose Graph Optimization: Duality, Optimal Solutions, and Verification

Pose graph optimization (PGO) is the problem of estimating a set of poses from pairwise relative measurements. PGO is a nonconvex problem and, currently, no known technique can guarantee the computation of a global optimal solution. In this paper, we show that Lagrangian duality allows computing a globally optimal solution under conditions that are satisfied in most robotics applications and enables to certify optimality of a given estimate. Our first contribution is to frame planar PGO in the complex domain. This makes analysis easier and allows drawing connections with existing literature on unit gain graphs. The second contribution is to formulate and analyze the properties of the Lagrangian dual problem in the complex domain. Our analysis shows that the duality gap is connected to the number of zero eigenvalues of the penalized pose graph matrix. We prove that if this matrix has a single zero eigenvalue, then 1) the duality gap is zero, 2) the primal PGO problem has a unique solution (up to an arbitrary roto-translation), and 3) the primal solution can be computed by scaling an eigenvector of the penalized pose graph matrix. The third contribution is algorithmic: We leverage duality to devise and algorithm that computes the optimal solution when the penalized matrix has a single zero eigenvalue. We also propose a suboptimal variant when the zero eigenvalues are multiple. Finally, we show that duality provides computational tools to verify if a given estimate (e.g., computed using iterative solvers) is globally optimal. We conclude the paper with an extensive numerical analysis. Empirical evidence shows that, in the vast majority of cases (100% of the tests under noise regimes of practical robotics applications), the penalized pose graph matrix has a single zero eigenvalue; hence, our approach allows computing (or verifying) the optimal solution.

Centroid precision and orientation precision of planar localization microscopy.

The concept of localization precision, which is essential to localization microscopy, is formally extended from optical point sources to microscopic rigid bodies. Measurement functions are presented to calculate the planar pose and motion of microscopic rigid bodies from localization microscopy data. Physical lower bounds on the associated uncertainties - termed centroid precision and orientation precision - are derived analytically in terms of the characteristics of the optical measurement system and validated numerically by Monte Carlo simulations. The practical utility of these expressions is demonstrated experimentally by an analysis of the motion of a microelectromechanical goniometer indicated by a sparse constellation of fluorescent nanoparticles. Centroid precision and orientation precision, as developed here, are useful concepts due to the generality of the expressions and the widespread interest in localization microscopy for super-resolution imaging and particle tracking.

A fast and accurate approximation for planar pose graph optimization

This work investigates the pose graph optimization problem, which arises in maximum likelihood approaches to simultaneous localization and mapping (SLAM). State-of-the-art approaches have been demonstrated to be very efficient in medium- and large-sized scenarios; however, their convergence to the maximum likelihood estimate heavily relies on the quality of the initial guess. We show that, in planar scenarios, pose graph optimization has a very peculiar structure. The problem of estimating robot orientations from relative orientation measurements is a quadratic optimization problem (after computing suitable regularization terms); moreover, given robot orientations, the overall optimization problem becomes quadratic. We exploit these observations to design an approximation of the maximum likelihood estimate, which does not require the availability of an initial guess. The approximation, named LAGO (Linear Approximation for pose Graph Optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima. We provide analytical results on existence and sub-optimality of LAGO, and we discuss the factors influencing its quality. Experimental results demonstrate that LAGO is accurate in common SLAM problems. Moreover, it is remarkably faster than state-of-the-art techniques, and is able to solve very large-scale problems in a few seconds.

Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles

In this paper, we describe a real-time algorithm for computing the ego-motion of a vehicle relative to the road. The algorithm uses as input only those images provided by a single omnidirectional camera mounted on the roof of the vehicle. The front ends of the system are two different trackers. The first one is a homography-based tracker that detects and matches robust scale-invariant features that most likely belong to the ground plane. The second one uses an appearance-based approach and gives high-resolution estimates of the rotation of the vehicle. This planar pose estimation method has been successfully applied to videos from an automotive platform. We give an example of camera trajectory estimated purely from omnidirectional images over a distance of 400 m. For performance evaluation, the estimated path is superimposed onto a satellite image. In the end, we use image mosaicing to obtain a textured 2-D reconstruction of the estimated path.

Detecting multiple texture planes using local spectral distortion

This paper presents a spectral method for identifying multiple texture planes. We commence by showing how pairs of spectral peaks can be used to make direct estimates of the slant and tilt angles. Our method commences by computing the affine distortion matrices for pairs of corresponding spectral peaks. We show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local planar pose. The leading eigenvector points in the tilt direction and the direction of the second eigenvector can be used to estimate the slant direction. By searching for clusters in the computed slant and tilt angles, we locate multiple texture planes. We apply the method to a variety of real-world and synthetic imagery. q 2002 Elsevier Science B.V. All rights reserved.