Maps of Dynamics

Kucner, Tomasz Piotr; Lilienthal, Achim J.; Magnusson, Martin; Palmieri, Luigi; Swaminathan, Chittaranjan Srinivas

doi:10.1007/978-3-030-41808-3_2

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…In particular, a robust algorithm can handle sensor noise, obstacles, and changing environments to ensure continuous and reliable operation ( Bongard, 2008 ). Additionally, accuracy is equally important for creating precise maps and localization, allowing the robot to make informed decisions and move through the environment without errors ( Kucner et al, 2023 ; Nakamura et al, 2023 ). These qualities collectively enhance the algorithm’s reliability in challenging real-world situations, making them crucial factors for successful mobile robotic applications.…”

Section: Guidelines For Evaluating and Selecting Visual Slam Methodsmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

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Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.

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Section: Guidelines For Evaluating and Selecting Visual Slam Methodsmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…MoDs can be classified into different groups based on what class of dynamics is being mapped. Figure 2 shows a categorization of dynamics based on the work by Kucner et al (2020a) . Several categories of dynamics are mentioned in Figure 2 and briefly discussed in the following.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking the utility of maps of dynamics for human-aware motion planning

et al. 2022

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Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

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Maps of Dynamics

Cited by 2 publications

References 36 publications

A review of visual SLAM for robotics: evolution, properties, and future applications

A review of visual SLAM for robotics: evolution, properties, and future applications

Benchmarking the utility of maps of dynamics for human-aware motion planning

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