GLFP: Global Localization from a Floor Plan

Marcotte, Ryan; Olson, Edwin

doi:10.1109/iros40897.2019.8968061

Cited by 20 publications

(5 citation statements)

References 13 publications

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“…Dirk Hähnel et al [ 47 ] presented an algorithm for acquiring 3D models with mobile robots. Xipeng Wang et al [ 48 ] devised a method for global localization using only schematic floor plans as prior maps. The method achieves global localization by matching features observed from LiDAR with features in the floor plan.…”

Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

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Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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“…Then they use a particle filter to estimate the robot pose by matching the extracted edges from the network and the digital floor plan. [29] performed global localization using schematic floor plans. They proposed a factor-graph-based localization approach that uses features -such as wall intersections and corners from the digital floor plans-as landmarks.…”

Section: B Localization In Architectural Plansmentioning

confidence: 99%

Robot Localization Using Situational Graphs (S-Graphs) and Building Architectural Plans

et al. 2023

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This paper presents robot localization using building architectural plans and hierarchical SLAM. We extract geometric, semantic as well as topological information from the architectural plans in the form of walls and rooms, and create the topological and metric-semantic layer of the Situational Graphs (S-Graphs) before navigating in the environment. When the robot navigates in the construction environment, it uses the robot odometry and 3D lidar measurements to extract planar wall surfaces. A particle filter method exploits the previously built situational graph and its available geometric, semantic, and topological information to perform global localization. We validate our approach in simulated and real datasets captured on ongoing construction sites presenting state-of-the-art results when comparing it against traditional geometry-based localization techniques.

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“…[26,10] used planar surfaces to efficiently align two lidar scans for loop closure detection. Li et al [15], Wang et al [23] used floor plan features such as corners and wall intersections for localization. Bae et al [3] proposed to use semantic features to detect and match corners of doors and walls.…”

Section: B Indoor Localizationmentioning

confidence: 99%

InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning

Zhang,

Digumarti,

Tinchev

et al. 2023

Robotics: Science and Systems XIX

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Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctive objects and structures. Mimicking the human approach, InstaLoc identifies and matches object instances in the scene with those from a prior map. As far as we know, this is the first method to use panoptic segmentation directly inferring on 3D lidar scans for indoor localization. InstaLoc operates through two networks based on spatially sparse tensors to directly infer dense 3D lidar point clouds. The first network is a panoptic segmentation network that produces object instances and their semantic classes. The second smaller network produces a descriptor for each object instance. A consensus based matching algorithm then matches the instances to the prior map and estimates a six degrees of freedom (DoF) pose for the input cloud in the prior map. InstaLoc utilizes two efficient networks, requires only one to two hours of training on a mobile GPU, and runs in real-time at 1 Hz. Our method achieves between two and four times more detections when localizing, as compared to baseline methods, and achieves higher precision on these detections.

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GLFP: Global Localization from a Floor Plan

Cited by 20 publications

References 13 publications

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Robot Localization Using Situational Graphs (S-Graphs) and Building Architectural Plans

InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning

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