scholarly journals Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 815
Author(s):  
Baifan Chen ◽  
Siyu Li ◽  
Haowu Zhao ◽  
Limei Liu
Keyword(s):  
Feature Matching ◽  
Transformation Matrix ◽  
Map Building ◽  
Collaborative Mapping ◽  
Matching Pair ◽  
The Right ◽  
Efficiency Map ◽  
Merging Method ◽  
Multi Robot ◽  
Map Merging

For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

scholarly journals Tomographic Feature-Based Map Merging for Multi-Robot Systems

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 107 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Gaussian Mixture Models ◽  
Search Space ◽  
Gaussian Mixture ◽  
Transformation Matrix ◽  
Robot Systems ◽  
Feature Based ◽  
The Individual ◽  
Merging Method ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 205-220 ◽  
Author(s):  
Heon-Cheol Lee ◽  
Seung-Hwan Lee ◽  
Myoung Hwan Choi ◽  
Beom-Hee Lee
Keyword(s):  
Gaussian Process ◽  
Particle Filter ◽  
Computer Simulations ◽  
Simultaneous Localization And Mapping ◽  
Transformation Matrix ◽  
Localization And Mapping ◽  
Probabilistic Map ◽  
Multi Robot ◽  
Map Merging

SUMMARYThis paper addresses the map merging problem, which is the most important issue in multi-robot simultaneous localization and mapping (SLAM) using the Rao–Blackwellized particle filter (RBPF-SLAM) with unknown initial poses. The map merging is performed using the map transformation matrix and the pair of map merging bases (MMBs) of the robots. However, it is difficult to find appropriate MMBs because each robot pose is estimated under multi-hypothesis in the RBPF-SLAM. In this paper, probabilistic map merging (PMM) using the Gaussian process is proposed to solve the problem. The performance of PMM was verified by reducing errors in the merged map with computer simulations and real experiments.

scholarly journals A Multi-robot System Coordination Design and Analysis on Wall Follower Robot Group

2018 ◽  
Vol 8 (6) ◽  
pp. 5098
Author(s):  
Agung Nugroho Jati ◽  
Randy Erfa Saputra ◽  
M. Ghozy Nurcahyadi ◽  
Nasy'an Taufiq Al Ghifary
Keyword(s):  
The Other ◽  
Communication Process ◽  
Time Process ◽  
Output Process ◽  
Robot System ◽  
Large Space ◽  
Ultrasound Sensor ◽  
The Right ◽  
Robot Group ◽  
Multi Robot

In this research, multi-robot formation can be established according to the environment or workspace. Group of robots will move sequently if there is no space for robots to stand side by side. Leader robot will be on the front of all robots and follow the right wall. On the other hand, robots will move side by side if there is a large space between them. Leader robot will be tracked the wall on its right side and follow on it while every follower moves side by side. The leader robot have to broadcast the information to all robots in the group in radius 9 meters. Nevertheless, every robot should be received information from leader robot to define their movements in the area. The error provided by fuzzy output process which is caused by read data from ultrasound sensor will drive to more time process. More sampling can reduce the error but it will drive more execution time. Furthermore, coordination time will need longer time and delay. Formation will not be establisehed if packet error happened in the communication process because robot will execute wrong command.

Strain History and Polar Decomposition

1996 ◽  
Author(s):  
Gerhard Oertel
Keyword(s):  
Polar Decomposition ◽  
Transformation Matrix ◽  
Strain History ◽  
Transformation Matrices ◽  
Before And After ◽  
The Matrix ◽  
Principal Stretches ◽  
Principal Directions ◽  
The Right ◽  
Asymmetric Transformation

The effect of two consecutive strains (only two states enter into the calculation of a strain, the states before and after, independently of the actual strain path) can be calculated by premultiplying the transformation matrix of the first strain (its stretch tensor) with that of the second. Unless the two strains are coaxial (their principal directions coincide), however, the resulting cumulative transformation matrix represents not only a strain but also a rigid-body rotation; in that case the matrix is asymmetric. The method of polar decomposition allows one to interpret the combined transformation as if it had come about either by a strain followed by a rotation (right polar decomposition) or by a rotation followed by a strain (left polar decomposition). Let 𝔸 and 𝔹 be two stretch tensors, or transformation matrices, representing each a strain without rotation; and let the strain 𝔹 follow the strain 𝔸. Then the combined transformation matrix 𝔽 is: . . . 𝔹𝔸 = 𝔽 = ℝ𝕌= 𝕍ℝ, (8.1) . . . where 𝔽 results from premultiplication of the earlier stretch 𝔸 with the later 𝔹, where ℝ𝕌 is the “right” and 𝕍ℝ the “left” decomposition of 𝔽, where 𝕌 and 𝕍 are two distinct stretch tensors, and where ℝ is the transformation matrix for a rotation (elements of rotation matrices are indicated by the symbol aij elsewhere in this book). 𝔽 is asymmetric and ℝ differs from the identity matrix (δij) except when 𝔸 and 𝔹 are coaxial. 𝕌 and 𝕍 have the same principal stretches and differ by orientation only. In Problems 120 to 122, false approaches in the search for an appropriate decomposition of an asymmetric transformation were recognized by yielding impossible values for a rotation. Application of eq. (8.1) makes such a trial-and-error approach unnecessary.

Method of Footprint Image Stitching Based on Multiscale SIFT Feature Matching

2013 ◽  
Vol 303-306 ◽  
pp. 1056-1059
Author(s):  
Sen Wang ◽  
Yin Hui Zhang ◽  
Zhong Hai Shi ◽  
Zi Fen He
Keyword(s):  
Wavelet Transform ◽  
Feature Matching ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Original Method ◽  
Inverse Transformation ◽  
Image Stitching ◽  
Image Splicing ◽  
Sift Feature ◽  
Matching Pair

The image stitching method is widely used into the suspect's footprint information extraction. In order to improve the image detail and the matching precision, the Footprint map image stitching method which is based on the wavelet transform and the SIFT feature matching is put forward. The wavelet transform in this method is perform based on the pretreatment of image, move the low frequency wavelet coefficient to zero, adjusting thresholds of the high frequency wavelet coefficient and inverse transformation, then, use the SIFT to extract and match the key-points of the processed images. For the error matching pair of coarse match, you can use the RANSAC to filter them out. This article demonstrates its advantage through to the original image splicing comparisons. The experimental results show that the method display more clear detail and the precision of matching than the original method.

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