Cooperative SLAM on small mobile robots

Waniek, Nicolai; Biedermann, Johannes; Conradt, Jörg

doi:10.1109/robio.2015.7419035

Cited by 20 publications

(14 citation statements)

References 13 publications

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“…Figure 13. (a) Embedded DVS128 on Pushbot as standalone closedloop perception-computation-action system, used in navigation and obstacle-avoidance tasks [240]. (b) Drone with a down-looking DAVIS, used for autonomous flight [27].…”

Section: Neuromorphic Computingmentioning

confidence: 99%

Event-Based Vision: A Survey

Gallego

Delbrück

Orchard

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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1,164

720

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Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of µs), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

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Section: Neuromorphic Computingmentioning

confidence: 99%

Event-Based Vision: A Survey

Gallego

Delbrück

Orchard

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

1,164

720

View full text Add to dashboard Cite

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“…Cooperative SLAM is one of the most researched subjects in the robotics field. Waniek et al 5 used the particle filter algorithm 6 for cooperatively solving the SLAM problem by two small mobile robots via only one remote computer. However, their works do not show one of the biggest challenges of multi-robot SLAM, that is, combining the information collected by multiple robots.…”

Section: Related Workmentioning

confidence: 99%

Cloud-based map alignment strategies for multi-robot FastSLAM 2.0

Ali

Hammad

Eldien

2019

International Journal of Distributed Sensor Networks

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The cooperative simultaneous localization and mapping problem has acquired growing attention over the years. Even though mapping of very large environments is theoretically quicker than a single robot simultaneous localization and mapping, it has several additional challenges such as the map alignment and the merging processes, network latency, administering various coordinate systems and assuring synchronized and updated data from all robots and also it demands massive computation. This article proposes an efficient architecture for cloud-based cooperative simultaneous localization and mapping to parallelize its complex steps via the multiprocessor (computing nodes) and free the robots from all of the computation efforts. Furthermore, this work improves the map alignment part using hybrid combination strategies, random sample consensus, and inter-robot observations to exploit fully their advantages. The results show that the proposed approach increases mapping performance with less response time.

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“…However, most cooperative approaches require huge computational resources and memory as a result of using visual sensing [17]. Cooperative SLAM on small mobile robots with limited sensing and computational resources have been studied in [18]. The robots however are equipped with laser pointers and vision sensors in order to compute dis-tances.…”

Section: Related Workmentioning

confidence: 99%

A framework for mapping with biobotic insect networks: From local to global maps

Dirafzoon

Bozkurt

Lobatón

2017

Robotics and Autonomous Systems

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We present an approach for global exploration and mapping of unknown environments using a swarm of cyborg insects, known as biobots, for emergency response scenarios under minimal sensing and localization constraints. We exploit natural stochastic motion models and controlled locomotion of biobots in conjunction with an aerial leader to explore and map a domain of interest. A sliding window strategy is adopted to construct local maps from coordinate free encounter information of the agents by means of local metric estimation. Robust topological features from these local representations are extracted using topological data analysis and a classification scheme. These maps are then merged into a global map which can be visualized using a graphical representation, that integrates geometric as well as topological features of the environment. Simulation and experimental results with biologically inspired robotic platform are presented to illustrate and verify the correctness of our approach, which provides building blocks for SLAM with biobotic insects.

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Cooperative SLAM on small mobile robots

Cited by 20 publications

References 13 publications

Event-Based Vision: A Survey

Event-Based Vision: A Survey

Cloud-based map alignment strategies for multi-robot FastSLAM 2.0

A framework for mapping with biobotic insect networks: From local to global maps

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