Autonomous indoor exploration with an event-based visual SLAM system

Hoffmann, Raoul; Weikersdorfer, David; Conradt, Jörg

doi:10.1109/ecmr.2013.6698817

Cited by 16 publications

(8 citation statements)

References 9 publications

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“…On-board pose estimation during flips and rolls of a quadrotor has been shown to be plausible using event-driven vision (Mueggler et al, 2015). Finally, robotic navigation and mapping systems include a real-time 2-DOF SLAM system for a mobile robot (Hoffmann et al, 2013), and 6-DOF parallel tracking and mapping algorithms (Kim et al, 2016;Rebecq et al, 2016).…”

Section: Event-driven Vision For Robotsmentioning

confidence: 99%

The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

et al. 2018

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Citation:Glover A, Vasco V, Iacono M and Bartolozzi C (2018) Event-driven (ED) cameras are an emerging technology that sample the visual signal based on changes in the signal magnitude, rather than at a fixed-rate over time. The change in paradigm results in a camera with a lower latency, that uses less power, has reduced bandwidth, and higher dynamic range. Such cameras offer many potential advantages for on-line, autonomous, robots; however, the sensor data do not directly integrate with current "image-based" frameworks and software libraries. The iCub robot uses Yet Another Robot Platform (YARP) as middleware to provide modular processing and connectivity to sensors and actuators. This paper introduces a library that incorporates an event-based framework into the YARP architecture, allowing event cameras to be used with the iCub (and other YARP-based) robots. We describe the philosophy and methods for structuring events to facilitate processing, while maintaining low-latency and real-time operation. We also describe several processing modules made available open-source, and three example demonstrations that can be run on the neuromorphic iCub.

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Section: Event-driven Vision For Robotsmentioning

confidence: 99%

The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

et al. 2018

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“…Probabilistic models and graphbased methods are typically used to integrate sensory inputs and update the map of the environment, delivering successful solutions to the SLAM problem [1]. However, when computational and power resources are limited, as in mobile applications, aerial vehicles, or robotic insects, more efficient solutions are needed to enable real-time processing and long operating time for embedded SLAM systems [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM

Kreiser

Renner

Sandamirskaya

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we investigate the use of ultra low-power, mixed signal analog/digital neuromorphic hardware for implementation of biologically inspired neuronal path integration and map formation for a mobile robot. We perform spiking network simulations of the developed architecture, interfaced to a simulated robotic vehicle. We then port the neuronal map formation architecture on two connected neuromorphic devices, one of which features on-board plasticity, and demonstrate the feasibility of a neuromorphic realization of simultaneous localization and mapping (SLAM).

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“…DVS-based solutions have been proposed for optic flow reconstruction [7,8], stereo [9], particle-filter based localization and mapping [10][11][12], active-landmarkbased pose tracking [13]. Contribution: In this work, we investigate the use of a DVS for visual odometry, together with a regular CMOS camera.…”

Section: Introductionmentioning

confidence: 99%

Low-latency event-based visual odometry

Censi

Scaramuzza

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

101

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Abstract-The agility of a robotic system is ultimately limited by the speed of its processing pipeline. The use of a Dynamic Vision Sensors (DVS), a sensor producing asynchronous events as luminance changes are perceived by its pixels, makes it possible to have a sensing pipeline of a theoretical latency of a few microseconds. However, several challenges must be overcome: a DVS does not provide the grayscale value but only changes in the luminance; and because the output is composed by a sequence of events, traditional frame-based visual odometry methods are not applicable. This paper presents the first visual odometry system based on a DVS plus a normal CMOS camera to provide the absolute brightness values. The two sources of data are automatically spatiotemporally calibrated from logs taken during normal operation. We design a visual odometry method that uses the DVS events to estimate the relative displacement since the previous CMOS frame by processing each event individually. Experiments show that the rotation can be estimated with surprising accuracy, while the translation can be estimated only very noisily, because it produces few events due to very small apparent motion.

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Autonomous indoor exploration with an event-based visual SLAM system

Cited by 16 publications

References 9 publications

The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM

Low-latency event-based visual odometry

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