A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Durdu, Akif; Korkmaz, Mehmet

doi:10.3906/elk-1807-335

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…The extracted features are usually considered static and do not change with time, ensuring that the extracted features can appear repeatedly in different local maps [26]. Common types of features include point features (e.g., scale-invariant feature transform (SIFT) features [24,44], speeded-up robust features (SURF) [45], and Harris [46]), line features (e.g., line segments [47], arcs [47]), and geometric features (e.g., rectangles [48]).…”

Section: Feature-based Methodsmentioning

confidence: 99%

“…None of the above feature-based methods has taken into account an extreme case-mismatching of features. This mismatching happens when two similar features in different maps are mistaken for the same feature [46]. Mismatching of features can often lead to serious map-merging faults, which commonly occur in environments with high repeatability such as symmetrically structured environments.…”

Section: Feature-based Methodsmentioning

confidence: 99%

“…Mismatching of features can often lead to serious map-merging faults, which commonly occur in environments with high repeatability such as symmetrically structured environments. To tackle this issue, Durdu and Korkmaz [46] proposed a semantic approach to fuse environments with symmetric parts. This approach effectively avoids erroneous feature matching in symmetric environments by evaluating the effectiveness of features.…”

Section: Feature-based Methodsmentioning

confidence: 99%

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A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Gostar

et al. 2020

Sensors

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When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

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Section: Feature-based Methodsmentioning

confidence: 99%

Section: Feature-based Methodsmentioning

confidence: 99%

Section: Feature-based Methodsmentioning

confidence: 99%

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A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Gostar

et al. 2020

Sensors

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“…In fact, the goal is just a robot moving in an unknown environment. For this, the environment must be mapped, and simultaneously, the robot must be localized within the constantly growing map [98]. If a mobile robot can solve the SLAM problem, that robot can move independently in the environment and perform tasks.…”

Section: Solution Proposal For Uav Applications In Greenhousesmentioning

confidence: 99%

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

et al. 2022

Self Cite

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The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

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“…Feature map: It relates geometric features (such as points, lines, and surfaces) to represent the environment, which is commonly used in Visual SLAM technology [45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar

Ismail

Roy

Sheu

et al. 2022

Sensors

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This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored floor of a building. We had implemented the e-SLAM (exploration-based SLAM) using the coordinate transformation and the navigation prediction techniques to achieve that purpose in the engineering school building which consists of many 100-m2 labs, corridors, elevator waiting space and the lobby. We first derive the LiDAR mesh for the orthogonal walls and filter out the static furniture and dynamic humans in the same space as the IMR. Then, we define the LiDAR pose frame including the translation and rotation from the orthogonal walls. According to the MSC (most significant corner) obtained from the intersection of the orthogonal walls, we calculate the displacement of the IMR. The orientation of the IMR is calculated from the alignment of orthogonal walls in the consecutive LiDAR pose frames, which is also assisted by the LQE (linear quadratic estimation) method. All the computation can be done in a single processor machine in real-time. The e-SLAM technique leads to a potential for the in-house service robot to start operation without having pre-scan LiDAR maps, which can save the installation time of the service robot. In this study, we use only the LiDAR and compared our result with the IMU to verify the consistency between the two navigation sensors in the experiments. The scenario of the experiment consists of rooms, corridors, elevators, and the lobby, which is common to most office buildings.

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A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Cited by 14 publications

References 23 publications

A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar

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