Multi-Robot Localization and Mapping Based on Signed Distance Functions

Koch, Philipp; May, Stefan; Schmidpeter, Michael; Kühn, Markus; Pfitzner, Christian; Merkl, Christian; Koch, Rainer; Fees, Martin; Martin, Jon; Ammon, Daniel; Nüchter, Andreas

doi:10.1007/s10846-016-0375-7

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…• Digital twin models in Cyber-Physical Manufacturing Systems (CPMS) [73], [74] • Analog Twin (AT) Framework for Human and AI Supervisory Control [75] • Experimentation in a remote laboratory setting [76] • Multi-robot localization and mapping [23], [77], [78] • Learning from observation [25] • Mobile manipulator positioning for object pick-up [26] • Navigation and obstacle avoidance [35], [36], [79]- [85] • Autonomous exploration in indoor environments [30], [86]- [88] • Immersive telepresence [89] • The specific application was not mentioned [27]- [29], [32], [53], [90]-[95] Healthcare…”

Section: Researchmentioning

confidence: 99%

“…They ensure that while a robot remains agile in its movements, it doesn't endanger its integrity or that of its environment. [116], ICP 3 + SVM 4 [132], 2D multi-SLAM [77], GMapping [87], [88], [105], FNN 5 [27], Visual-SLAM + RTAB-Map 6 [126], RTAB-Map [94], Cartographer [65], Monocular SLAM + RL [36], KimeraMulti [133] 2 -Object Detection YOLOv4 7 [62], [103], YOLOv3 [31], [59], [62], YOLOv2 [134], PointNet [135], CNN 8 [21], [23], [136], SVM [137], MobileNet [138], YOLOv2 + JPDA 9 + IMM 10 [139] 3 -Object Tracking…”

Section: Householdmentioning

confidence: 99%

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Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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Section: Researchmentioning

confidence: 99%

Section: Householdmentioning

confidence: 99%

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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“…For mapping applications, SDFs have experienced a resurgence in recent years where they have proven useful for aggregating visual data from consumer-grade depth cameras [3]. Following this development, several recent works [20], [21] have investigated the utility of SDFs in the front end of 2D lidar mapping systems. This paper is an investigation into the utility of this representation for place-recognition in that context.…”

Section: Signed Distance Functionsmentioning

confidence: 99%

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

Millane

Oleynikova

Nieto

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry.

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“…The critical innovation point of reference [20] is the design of mechanism which allows multiple threads to concurrently read and modify the same map for multiple micro aerial vehicles. Lecture [21] describes strategy using data of 2D LIDAR sensors to build a joint map instead of occasional merging of smaller maps.…”

Section: Related Workmentioning

confidence: 99%

SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

et al. 2018

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Abstract:The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

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Multi-Robot Localization and Mapping Based on Signed Distance Functions

Cited by 22 publications

References 13 publications

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

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