scholarly journals SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

2018 ◽  
Vol 10 (8) ◽  
pp. 2946
Author(s):  
Zhaoyi Pei ◽  
Songhao Piao ◽  
Mohammed Souidi ◽  
Muhammad Qadir ◽  
Guo Li
Keyword(s):  
Closed Loop ◽  
Image Features ◽  
Bundle Adjustment ◽  
Service Robots ◽  
Global Level ◽  
Complex Environment ◽  
Localization And Mapping ◽  
Fundamental Research ◽  
Independent Mode ◽  
Multi Robot

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

scholarly journals A real-time semantic visual SLAM approach with points and objects

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090544
Author(s):  
Peiyu Guan ◽  
Zhiqiang Cao ◽  
Erkui Chen ◽  
Shuang Liang ◽  
Min Tan ◽  
...  
Keyword(s):  
Real Time ◽  
Object Segmentation ◽  
Bundle Adjustment ◽  
Service Robots ◽  
Object A ◽  
Accurate Localization ◽  
Localization And Mapping ◽  
Point Object ◽  
Environment Perception ◽  
Object Association

Visual simultaneously localization and mapping (SLAM) is important for self-localization and environment perception of service robots, where semantic SLAM can provide a more accurate localization result and a map with abundant semantic information. In this article, we propose a real-time PO-SLAM approach with the combination of both point and object measurements. With point–point association in ORB-SLAM2, we also consider point–object association based on object segmentation and object–object association, where the object segmentation is employed by combining object detection with depth histogram. Also, besides the constraint of feature points belonging to an object, a semantic constraint of relative position invariance among objects is introduced. Accordingly, two semantic loss functions with point and object information are designed and added to the bundle adjustment optimization. The effectiveness of the proposed approach is verified by experiments.

Multi-Robot SLAM in Dynamic Environments with Parallel Maps

2021 ◽  
pp. 2150011
Author(s):  
Sajad Badalkhani ◽  
Ramazan Havangi ◽  
Mohsen Farshad
Keyword(s):  
Large Scale ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Extensive Literature ◽  
Real World Data ◽  
Data Set ◽  
Cooperative Approach ◽  
Localization And Mapping ◽  
Multi Robot

There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

Automatically Annotated Mapping for Indoor Mobile Robot Applications

2012 ◽  
Author(s):  
Ali Gürcan Özkil ◽  
Thomas Howard
Keyword(s):  
Mobile Robot ◽  
Practical Method ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Indoor Environments ◽  
And Topology ◽  
Localization And Mapping ◽  
Indoor Mobile Robot ◽  
Mobile Service Robots

This paper presents a new and practical method for mapping and annotating indoor environments for mobile robot use. The method makes use of 2D occupancy grid maps for metric representation, and topology maps to indicate the connectivity of the ‘places-of-interests’ in the environment. Novel use of 2D visual tags allows encoding information physically at places-of-interest. Moreover, using physical characteristics of the visual tags (i.e. paper size) is exploited to recover relative poses of the tags in the environment using a simple camera. This method extends tag encoding to simultaneous localization and mapping in topology space, and fuses camera and robot pose estimations to build an automatically annotated global topo-metric map. It is developed as a framework for a hospital service robot and tested in a real hospital. Experiments show that the method is capable of producing globally consistent, automatically annotated hybrid metric-topological maps that is needed by mobile service robots.

scholarly journals Multitarget Search of Swarm Robots in Unknown Complex Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
You Zhou ◽  
Anhua Chen ◽  
Hongqiang Zhang ◽  
Xin Zhang ◽  
Shaowu Zhou
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Force Model ◽  
Complex Environments ◽  
Target Response ◽  
Complex Environment ◽  
Multiple Targets ◽  
Virtual Force ◽  
Swarm Robots ◽  
Division Model

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

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