Abstract

Aiming at the problem that traditional methods cannot meet the problem of synchronous positioning of multi-mobile robots in different motion states, a synchronous positioning method of multi-mobile robots based on visual simultaneous localization and mapping (SLAM) algorithm is proposed. Analyze the general model of SLAM problem, realize visual SLAM feature detection through scale invariant feature transform algorithm, and match image features based on active vision. A multi-mobile robot motion state estimation model is constructed, an octree model is used to construct a multi-robot synchronous localization map, and the camera pose tracking is realized by using the oriented fast accelerated segment test and rotated binary robust independent elementary features points that are not in the target bounding box. The three-dimensional points in the environment recovered by visual SLAM separate the background target and the moving target, and match the corresponding target in the previous frame through the feature to realize the synchronous positioning of the multi-mobile robot. The experimental results show that the method can realize the synchronous positioning of multi-robots under the state of multi-robot linear motion, curvilinear motion and mixed motion, and the positioning accuracy is high.

Issue Section:
Technical Papers
Keywords:
visual simultaneous localization and mapping algorithm, multi-mobile robot, simultaneous localization, target feature extraction

References

1.
Zhang
 
X. N.
 and
Zhang
 
S. F.
, “
High-Precision Indoor Dynamic Target Positioning Method Based on GNSS/SLAM Combination
” (in Chinese),
Jisuanji Fangzhen
 
38
, no. 
3
(March
2021
):
465
–
469
, https://doi.org/10.3969/j.issn.1006-9348.2021.03.095
2.
Chen
 
Y.
,
Huang
 
S.
,
Zhao
 
L.
, and
Dissanayake
 
G.
, “
Cramér–Rao Bounds and Optimal Design Metrics for Pose-Graph SLAM
,”
IEEE Transactions on Robotics
 
37
, no. 
2
(April
2021
):
627
–
641
, https://doi.org/10.1109/TRO.2020.3001718
Google Scholar
Crossref
Search ADS
 
3.
Zhou
 
B.
,
He
 
Y.
,
Qian
 
K.
,
Ma
 
X.
, and
Li
 
X.
, “
S4-SLAM: A Real-Time 3D LIDAR SLAM System for Ground/Watersurface Multi-scene Outdoor Applications
,”
Autonomous Robots
 
45
, no. 
1
(January
2021
):
77
–
98
, https://doi.org/10.1007/s10514-020-09948-3
Google Scholar
Crossref
Search ADS
 
4.
Zheng
 
L. L.
,
Sun
 
W.
, and
Li
 
M. M.
, “
Fast Localization Method of Autonomous Mobile Robots Based on Particle Filtering
” (in Chinese),
Transducer and Microsystem Technologies
 
39
, no. 
10
(October
2020
):
31
–
34
, https://doi.org/10.13873/J.1000-9787(2020)10-0031-04
5.
Chen
 
C.
,
Zang
 
W. W.
, and
Xu
 
J.
, “
A Method of Mobile Robot Localization and Mapping Based on 3D Vision
” (in Chinese),
Modern Electronics Technique
 
43
, no. 
6
(June
2020
): 34–38+42, https://doi.org/10.16652/j.issn.1004-373x.2020.06.009
6.
Leitinger
 
E.
,
Meyer
 
F.
,
Hlawatsch
 
F.
,
Witrisal
 
K.
,
Tufvesson
 
F.
, and
Win
 
M. Z.
, “
A Belief Propagation Algorithm for Multipath-Based SLAM
,”
IEEE Transactions on Wireless Communications
 
18
, no. 
12
(December
2019
):
5613
–
5629
, https://doi.org/10.1109/TWC.2019.2937781
Google Scholar
Crossref
Search ADS
 
7.
Mutti
 
S.
,
Nicola
 
G.
,
Beschi
 
M.
,
Pedrocchi
 
N.
, and
Tosatti
 
L. M.
, “
Towards Optimal Task Positioning in Multi-robot Cells, Using Nested Meta-heuristic Swarm Algorithms
,”
Robotics and Computer-Integrated Manufacturing
 
71
(October
2021
): 102131, https://doi.org/10.1016/j.rcim.2021.102131
8.
Mahon
 
R.
,
Moore
 
C. I.
,
Ferraro
 
M. S.
,
Rabinovich
 
W. S.
, and
Frederickson
 
P. A.
, “
Comparison of Maritime Measurements of Cn2 with NAVSLaM Model Predictions
,”
Applied Optics
 
59
, no. 
33
(November
2020
):
10599
–
10612
, https://doi.org/10.1364/AO.405185
Google Scholar
Crossref
Search ADS
PubMed
 
9.
Street
 
C.
,
Pütz
 
S.
,
Mühlig
 
M.
,
Hawes
 
N.
, and
Lacerda
 
B.
, “
Congestion-Aware Policy Synthesis for Multirobot Systems
,”
IEEE Transactions on Robotics
 
38
, no. 
1
(February
2022
):
262
–
280
, https://doi.org/10.1109/TRO.2021.3071618
Google Scholar
Crossref
Search ADS
 
10.
Fan
 
Y.
,
Zhang
 
Q.
,
Tang
 
Y.
,
Liu
 
S.
, and
Han
 
H.
, “
Blitz-SLAM: A Semantic SLAM in Dynamic Environments
,”
Pattern Recognition
 
121
(January
2022
): 108225, https://doi.org/10.1016/j.patcog.2021.108225
11.
Liu
 
Y.-T.
,
Sun
 
R.-Z.
,
Zhang
 
X.-N.
,
Li
 
L.
, and
Shi
 
G.-Q.
, “
An Autonomous Positioning Method for Fire Robots with Multi-source Sensors
,”
Wireless Networks
 
33
, no. 
2
(
2021
): https://doi.org/10.1007/s11276-021-02566-6
12.
Darvish
 
K.
,
Simetti
 
E.
,
Mastrogiovanni
 
F.
, and
Casalino
 
G.
, “
A Hierarchical Architecture for Human-Robot Cooperation Processes
,”
IEEE Transactions on Robotics
 
37
, no. 
2
(April
2021
):
567
–
586
, https://doi.org/10.1109/TRO.2020.3033715
Google Scholar
Crossref
Search ADS
 
13.
An
 
J.
 and
Lee
 
J.
, “
Robust Positioning and Navigation of a Mobile Robot in an Urban Environment Using a Motion Estimator
,”
Robotica
 
37
, no. 
8
(August
2019
):
1320
–
1331
, https://doi.org/10.1017/S0263574718001534
Google Scholar
Crossref
Search ADS
 
14.
Luo
 
G.
,
Zou
 
L.
,
Wang
 
Z.
,
Lv
 
C.
,
Ou
 
J.
, and
Huang
 
Y.
, “
A Novel Kinematic Parameters Calibration Method for Industrial Robot Based on Levenberg-Marquardt and Differential Evolution Hybrid Algorithm
,”
Robotics and Computer-Integrated Manufacturing
 
71
(October
2021
): 102165, https://doi.org/10.1016/j.rcim.2021.102165
15.
Güler
 
S.
,
Abdelkader
 
M.
, and
Shamma
 
J. S.
, “
Peer-to-Peer Relative Localization of Aerial Robots with Ultrawideband Sensors
,”
IEEE Transactions on Control Systems Technology
 
29
, no. 
5
(September
2021
):
1981
–
1996
, https://doi.org/10.1109/TCST.2020.3027627
Google Scholar
Crossref
Search ADS
 
This content is only available via PDF.
All rights reserved. This material may not be reproduced or copied, in whole or part, in any printed, mechanical, electronic, film, or other distribution and storage media, without the written consent of ASTM International.
You do not currently have access to this content.