Distributed Multi Robot Simultaneous Localization and Mapping with Consensus Particle Filtering

Hidayat, Fadhil; Trilaksono, Bambang Riyanto; Hindersah, Hilwadi

doi:10.1088/1742-6596/801/1/012003

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Computation of , is shown on equation (27 However, the calculations that are given in equations (28) to (31) require rearrangement of the particle index owned by robot and ′. The set of particles of both robot and ′ are sorted based on their local significant index.…”

Section: F Consensus-based Calculation Of Particles Weight In Distrimentioning

confidence: 99%

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Hidayat¹,

Trilaksono²,

Hindersyah³

2017

ijeei

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Extensive research on single-robot simultaneous localization and mapping (SLAM) over the past decade has provided good estimation results in mapping small environment. This raises the idea for building a larger map by using some robots that are assembled within the team, also called multi-robot SLAM. There are two types of multirobot SLAM, that is, centralized and distributed multi-robot SLAM. Centralized multirobot SLAM method still has problems in case of system failure on some robots, especially the system failure on the robot central. Distributed multi-robot SLAM was developed to overcome the weaknesses in centralized multi-robot SLAM by: 1) building a map of larger environment by fusing local maps from a group of robots, 2) eliminating the dependence on central processing by using distributed computing, 3) overcoming the vulnerability in case of system failure in some robots, especially the system failure in the robot central, and 4) eliminating centralized communication, in which each robot requires only local communication with its neighboring robots. However, the distributed multirobot SLAM method has not yielded good results on map estimates and localization estimates. This paper proposes consensus-based distributed multi-robot SLAM method that can be applied to map common environments. We use FastSLAM algorithm as the main base in developing distributed multi-robot SLAM. We suggest using one of two selection of consensus parameters, that is, particle weight and posterior parameter. We assume that each robot has the same motion model and observation model; therefore, the system noise and observation noise is the same. The aim of this paper is to design consensus based distributed multi-robot SLAM method that provides better map estimation and localization results when applied to common environments. We concentrate to test the proposed methods on aspects: 1) root mean square error (RMSE) of map to see the map estimation performance, 2) RMSE of localization to see the robot's pose estimation performance, 3) mapping coverage to see how long it will take until all features are observed, and 4) processing time to see how long the computation process takes place per timestep.

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Section: F Consensus-based Calculation Of Particles Weight In Distrimentioning

confidence: 99%

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Hidayat¹,

Trilaksono²,

Hindersyah³

2017

ijeei

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“…The most important challenge in cooperation SLAM is how to stitch the maps from multiple tasks or multiple robots together by determining the relative positioning between multiple mapping results [22,23]; because SLAM is a relative positioning process, the coordinate system of the mapping result is usually determined by the first frame of data. There have been many studies on estimating the relative position, such as by seeking overlap of the local map [24,25], adding special sensors which can make each robot see the others [22,26], and cooperative localization methods [27][28][29]. Although these methods can achieve distributed indoor mapping, they depend on accurate matching algorithms to determine the relative positioning, which requires adjacent areas to have sufficient overlap, reduces mapping efficiency, and increases hardware costs.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis

2020

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Indoor mobile mapping techniques are important for indoor navigation and indoor modeling. As an efficient method, Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) has been applied for fast indoor mobile mapping. It can quickly construct high-precision indoor maps in a certain small region. However, with the expansion of the mapping area, SLAM-based mapping methods face many difficulties, such as loop closure detection, large amounts of calculation, large memory occupation, and limited mapping precision. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM to solve the problem of mapping for large-scale environments. Its effectiveness is analyzed from the relative accuracy and absolute accuracy of the mapping results. The experimental results show that the relative accuracy can reach 0.08 m, an improvement of 49.8% compared to the mapping result without loop closure. The absolute accuracy can reach 0.13 m, which proves the method’s feasibility for distributed mapping. The accuracies under different numbers of control points are also compared to find the suitable structure of the control network.

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Multi-robot CSLAM in the Unknown Environment

Zhang

et al. 2018

2018 IEEE International Conference on Information and Automation (ICIA)

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Distributed Multi Robot Simultaneous Localization and Mapping with Consensus Particle Filtering

Cited by 3 publications

References 12 publications

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis

Multi-robot CSLAM in the Unknown Environment

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