Boundary Wire Mapping on Autonomous Lawn Mowers

Einecke, Nils; Deigmöller, Jörg; Muro, Keiji; Franzius, Mathias

doi:10.1007/978-3-319-67361-5_23

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…2) using a standard lawn mower wire guidance technology. For this working period, it is possible to obtain highly precise metric coordinates (x, y) with odometry using loop closure and subsequent error correction [21]. In the second working period, the robot traverses freely within the premises of the area enclosed by border wire.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Fast Visual Localization and Mapping with Slow Features

Haris

Franzius

Bauer-Wersing

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Visual localization is the task of accurately estimating the camera's position in a known environment. State-of-the-art methods use the 3D structure of a scene for precise visual localization. However, 3D scene reconstruction is resourceintensive in terms of hardware requirements and computation time, making it infeasible to run on low-cost embedded hardware. Unsupervised spatial representation learning with Slow Feature Analysis (SFA) enables computationally inexpensive localization and mapping. This paper analyzes SFA-based and the well-known structure-based localization, i.e., Active Search, in two distinct settings: short-term temporal and extreme spatial generalization. We present the experimental results from an outdoor environment and compare both methods w.r.t localization accuracy and computation time. Results show that the SFA-based approach is 886x faster in mapping time and 34x faster in localization than Active Search while achieving comparable localization accuracy in our test scenario.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Fast Visual Localization and Mapping with Slow Features

Haris

Franzius

Bauer-Wersing

2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…Offline methods requires knowledge about the entire environment, while online methods plans the coverage path while exploring it. [6] The CPP problem for covering indoor environments [6], croplands [17] and lawns [10] is a well studied problem with established solutions. These environments have a flat surface and are often easily dividable into rooms.…”

Section: Motivationmentioning

confidence: 99%

Coverage Path Planning in Large-scale Multi-floor Urban Environments with Applications to Autonomous Road Sweeping

Engelsons,

Tiger,

Heintz

2022

2022 International Conference on Robotics and Automation (ICRA)

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Detta dokument hålls tillgängligt på Internet -eller dess framtida ersättare -under år från publiceringsdatum under förutsättning att inga extraordinära omständigheter uppstår.Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns lösningar av teknisk och administrativ art.Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsmannens litterära eller konstnärliga anseende eller egenart.För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/.

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“…Autonomous robots are ubiquitous nowadays and expected to grow in popularity and deployment well into the future. In some application domains, such as vacuum cleaning, lawn mowing, and painting, it is of interest for an intelligent autonomous robot to be able to cover the entirety of a given environment [1,2,3,4,5]. Hence, the robot should be able to plan a path so as to visit the entire environment not occupied by an obstacle, while at the same time controlling some notions of cost such as revisiting already covered areas, i.e., overlap, coverage time, number of turns, or energy consumption [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition

Heydari,

Saha,

Ganapathy

2021

Preprint

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Coverage path planning in a generic known environment is shown to be NP-hard. When the environment is unknown, it becomes more challenging as the robot is required to rely on its online map information built during coverage for planning its path. A significant research effort focuses on designing heuristic or approximate algorithms that achieve reasonable performance. Such algorithms have sub-optimal performance in terms of covering the area or the cost of coverage, e.g., coverage time or energy consumption. In this paper, we provide a systematic analysis of the coverage problem and formulate it as an optimal stopping time problem, where the trade-off between coverage performance and its cost is explicitly accounted for. Next, we demonstrate that reinforcement learning (RL) techniques can be leveraged to solve the problem computationally. To this end, we provide some technical and practical considerations to facilitate the application of the RL algorithms and improve the efficiency of the solutions. Finally, through experiments in grid world environments and Gazebo simulator, we show that reinforcement learning-based algorithms efficiently cover realistic unknown indoor environments, and outperform the current state of the art.

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Boundary Wire Mapping on Autonomous Lawn Mowers

Cited by 8 publications

References 17 publications

Unsupervised Fast Visual Localization and Mapping with Slow Features

Unsupervised Fast Visual Localization and Mapping with Slow Features

Coverage Path Planning in Large-scale Multi-floor Urban Environments with Applications to Autonomous Road Sweeping

Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition

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