Multi-robot Exploration and Mapping Based on the Subdefinite Models

Карпов, В. Э.; Migalev, A. S.; Moscowsky, Anton; Rovbo,; Vorobiev, Vitaly

doi:10.1007/978-3-319-43955-6_18

Cited by 7 publications

(1 citation statement)

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“…The most popular approach to exploration is frontier-based exploration introduced by Yamauchi [25] and further extended by many researchers [24,2,1,11,14]. The approach is based on occupancy grids where the working space is divided into small cells and each cell stores information about the corresponding piece of the environment in the form of a probabilistic estimate of its state.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Autonomous Environment Modeling

Kulich

Kozák

Přeučil

2018

Modelling and Simulation for Autonomous Systems

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In this paper, we present an integrated solution to memoryefficient environment modeling by an autonomous mobile robot equipped with a laser range-finder. Majority of nowadays approaches to autonomous environment modelling, called exploration, employs occupancy grids as environment representation where the working space is divided into small cells each storing information about the corresponding piece of the environment in the form of a probabilistic estimate of its state. In contrast, the presented approach uses a polygonal representation of the explored environment which consumes much less memory, enables fast planning and decisionmaking algorithms and it is thus reliable for large-scale environments. Simultaneous localization and mapping (SLAM) has been integrated into the presented framework to correct odometry errors and to provide accurate position estimates. This involves also refinement of the already generated environment model in case of loop closure, i.e. when the robot detects that it revisited an already explored place. The framework has been implemented in Robot Operating System (ROS) and tested with a real robot in various environments. The experiments show that the polygonal representation with SLAM integrated can be used in the real world as it is fast, memory efficient and accurate. Moreover, the refinement can be executed in real-time during the exploration process.

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