Integration of affine ICP into the precise localization problem of smart-AGVs: Procedures, enhancements and challenges

2020 ◽  
pp. 014233122093343
Author(s):  
Abdurrahman Yilmaz ◽  
Hakan Temeltas
Keyword(s):  
Indoor Environments ◽  
Precise Localization ◽  
Localization Problem ◽  
Point Sets ◽  
Registration Method ◽  
Point Set Registration ◽  
Rotation Plane ◽  
Point Set ◽  
Localization Performance ◽  
Industrial Standards

The localization problem in robotics has been widely studied both for indoor and outdoor applications, but is still open for improvements. In indoor environments, GPS-based methods are not preferred due to reflections, and the pose of the robot is determined according to the measurements taken around with its sensors. One of them is iterative closest point (ICP)-based localization method. ICP is a point set registration method, the essence of which is to iteratively compute the transformation between two point sets. However, it is also utilized to solve the localization problem thanks to its high precision in registration. Precise localization is important for applications that require highly accurate pose estimation, such as for smart-AGVs to be used in smart factories to reach a station at industrial standards. Traditional ICP finds transformation in terms of a rotation and translation, and thus can be directly applied to the localization problem. On the other hand, the affine variant of ICP is not adapted to solve the localization problem. In this study, the necessary arrangements to make affine ICP suitable for precise localization are given as a procedure such that the transformation between point sets is found by affine ICP, the resulting transformation is projected to rotation plane by polar decomposition and then the pose is estimated. The enhancements achieved with the usage of affine ICP in precise localization problems are demonstrated in simulation by comparing localization performance of affine ICP with that of traditional ICP. For this purpose, in a factory environment, a scenario where a smart-AGV approaching the target autonomously to carry out an operation has been prepared. The performances of the algorithms have been evaluated for five different docking stations with 30 separate experiments. Moreover, the challenges related to the affine ICP-based fine localization, in particular about finding projection of affine transformation to rotation plane, are highlighted in this study.

A robust non-rigid point set registration method based on inhomogeneous Gaussian mixture models

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

Point set registration for assembly feature pose estimation using simulated annealing nested Gauss-Newton optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kunyong Chen ◽  
Yong Zhao ◽  
Jiaxiang Wang ◽  
Hongwen Xing ◽  
Zhengjian Dong
Keyword(s):  
Simulated Annealing ◽  
Pose Estimation ◽  
Distance Metric ◽  
Implicit Representation ◽  
Registration Method ◽  
Content Type ◽  
Point Set Registration ◽  
Searching Strategy ◽  
Global Robustness ◽  
Point Set

Purpose This paper aims to propose a fast and robust 3D point set registration method for pose estimation of assembly features with few distinctive local features in the manufacturing process. Design/methodology/approach The distance between the two 3D objects is analytically approximated by the implicit representation of the target model. Specifically, the implicit B-spline surface is adopted as an interface to derive the distance metric. With the distance metric, the point set registration problem is formulated into an unconstrained nonlinear least-squares optimization problem. Simulated annealing nested Gauss-Newton method is designed to solve the non-convex problem. This integration of gradient-based optimization and heuristic searching strategy guarantees both global robustness and sufficient efficiency. Findings The proposed method improves the registration efficiency while maintaining high accuracy compared with several commonly used approaches. Convergence can be guaranteed even with critical initial poses or in partial overlapping conditions. The multiple flanges pose estimation experiment validates the effectiveness of the proposed method in real-world applications. Originality/value The proposed registration method is much more efficient because no feature estimation or point-wise correspondences update are performed. At each iteration of the Gauss–Newton optimization, the poses are updated in a singularity-free format without taking the derivatives of a bunch of scalar trigonometric functions. The advantage of the simulated annealing searching strategy is combined to improve global robustness. The implementation is relatively straightforward, which can be easily integrated to realize automatic pose estimation to guide the assembly process.

Affine ICP for Fine Localization of Smart-AGVs in Smart Factories

2019 ◽  
Author(s):  
Abdurrahman Yılmaz ◽  
Hakan Temeltaş
Keyword(s):  
Least Square ◽  
Automated Guided Vehicles ◽  
Translation Vector ◽  
Point Sets ◽  
Registration Method ◽  
Point Set Registration ◽  
Localization Algorithms ◽  
Load Carrying ◽  
Smart Factories ◽  
Fine Localization

Abstract With the emergence of the concept of Industry 4.0, smart factories have started to be planned in which the production paradigm will change. Automated Guided Vehicles, abbreviated as AGV, that will perform load carrying and similar tasks in smart factories, Smart-AGVs, will try to reach their destinations on their own route instead of predetermined routes like in today’s factories. Moreover, since they will not reach their targets in a single way, they have to dock a target with their fine localization algorithms. In this paper, an affine Iterative Closest Point, abbreviated as ICP, based fine localization method is proposed, and applied on Smart-AGV docking problem in smart factories. ICP is a point set registration method but it is also used for localization applications due to its high precision. Affine ICP is an ICP variant which finds affine transformation between two point sets. In general, the objective function of ICP is constructed based on least square metric. In this study, we use affine ICP with correntropy metric. Correntropy is a similarity measure between two random variables, and affine ICP with correntropy tries to maximize the similarity between two point sets. Affine ICP has never been utilized in fine localization problem. We make an update on affine ICP by means of polar decomposition to reach transformation between two point sets in terms of rotation matrix and translation vector. The performance of the algorithm proposed is validated in simulation and the efficiency of it is demonstrated on MATLAB by comparing with the docking performance of the traditional ICP.

Robust point set registration method based on global structure and local constraints

2019 ◽  
Vol 5 (2) ◽  
pp. 76
Author(s):  
Yufei Chen ◽  
Kai Yang ◽  
Haotian Zhang ◽  
Xianhui Liu ◽  
Weidong Zhao
Keyword(s):  
Global Structure ◽  
Local Constraints ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals Globally Optimal Point Set Registration by Joint Symmetry Plane Fitting

2021 ◽  
Author(s):  
Lan Hu ◽  
Laurent Kneip
Keyword(s):  
State Of The Art ◽  
Symmetry Plane ◽  
Point Sets ◽  
Optimal Point ◽  
Point Set Registration ◽  
Registration Process ◽  
Current State ◽  
Plane Fitting ◽  
Point Set ◽  
Dense 3D Reconstruction

AbstractThe present work proposes a solution to the challenging problem of registering two partial point sets of the same object with very limited overlap. We leverage the fact that most objects found in man-made environments contain a plane of symmetry. By reflecting the points of each set with respect to the plane of symmetry, we can largely increase the overlap between the sets and therefore boost the registration process. However, prior knowledge about the plane of symmetry is generally unavailable or at least very hard to find, especially with limited partial views. Finding this plane could strongly benefit from a prior alignment of the partial point sets. We solve this chicken-and-egg problem by jointly optimizing the relative pose and symmetry plane parameters. We present a globally optimal solver by employing the branch-and-bound paradigm and thereby demonstrate that joint symmetry plane fitting leads to a great improvement over the current state of the art in globally optimal point set registration for common objects. We conclude with an interesting application of our method to dense 3D reconstruction of scenes with repetitive objects.

scholarly journals GO-APSR: A Globally Optimal Affine Point Set Registration Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137232-137240 ◽  
Author(s):  
Xuechen Li ◽  
Yinlong Liu ◽  
Manning Wang ◽  
Zhijian Song
Keyword(s):  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals Robust non‐rigid point set registration method based on asymmetric Gaussian and structural feature

2018 ◽  
Vol 12 (6) ◽  
pp. 806-816 ◽  
Author(s):  
Jun Dou ◽  
Dongmei Niu ◽  
Zhiquan Feng ◽  
Xiuyang Zhao
Keyword(s):  
Structural Feature ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set
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