A Practical Multirobot Localization System

Krajník, Tomáš; Nitsche, Matıas; Faigl, Jan; Vaněk, Petr; Saska, Martin; Přeučil, Libor; Duckett, Tom; Mejail, Marta

doi:10.1007/s10846-014-0041-x

Cited by 189 publications

(142 citation statements)

References 23 publications

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“…Its navigation was based on the ROS navigation stack, which used our FreMEn 2D grid instead of the traditional map server. To achieve autonomous operation, the robot uses a precise visual servoing method for reliable docking to its charging station [20]. Our evaluation was based on three criteria: localization accuracy, navigation efficiency and map quality.…”

Section: Methodsmentioning

confidence: 99%

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

Krajník

Fentanes

Hanheide

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We present a lifelong mapping and localisation system for long-term autonomous operation of mobile robots in changing environments. The core of the system is a spatiotemporal occupancy grid that explicitly represents the persistence and periodicity of the individual cells and can predict the probability of their occupancy in the future. During navigation, our robot builds temporally local maps and integrates then into the global spatio-temporal grid. Through re-observation of the same locations, the spatio-temporal grid learns the longterm environment dynamics and gains the ability to predict the future environment states. This predictive ability allows to generate time-specific 2d maps used by the robot's localisation and planning modules. By analysing data from a long-term deployment of the robot in a human-populated environment, we show that the proposed representation improves localisation accuracy and the efficiency of path planning. We also show how to integrate the method into the ROS navigation stack for use by other roboticists.

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Section: Methodsmentioning

confidence: 99%

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

Krajník

Fentanes

Hanheide

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In our experiments, all the aggregation cues are circular light spots with maximum illuminance of 420 lux. We use visual localisation software [12] to track the robots.…”

Section: Methodsmentioning

confidence: 99%

Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

Arvin

Attar

Turgut

et al. 2015

Lecture Notes in Computer Science

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Abstract. Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.

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“…Therefore, we use an algorithm that allows for rapid detection and localization of simple circular patterns composed of concentric black and white circles of known diameter. Our algorithm (details described in [36]) outperforms common black-andwhite pattern detectors in terms of speed by an order of magnitude while achieving similar precision and robustness. An example of the localization pattern with a sketch of the possible operational space of the relative localization module is depicted in Fig.…”

Section: System For Relative Localizationmentioning

confidence: 95%

“…For details and experiments identifying the sensor model, see [38], [36]. Except the viewing angle, which can be clearly defined for each optical system (based on the lens), the most important factors that need to be considered in swarm stabilization and motion planning are the measurement accuracy and reliability.…”

Section: A Relative Localization System Performancementioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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A Practical Multirobot Localization System

Cited by 189 publications

References 23 publications

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

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