A Review of Bioinspired Vision Sensors and Their Applications

Cho, Dong‐il Dan; Lee, Tae-jae

doi:10.18494/sam.2015.1083

Cited by 12 publications

(10 citation statements)

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“…(Middle) A range of applications based on visual-tactile fusion perception, including object recognition, human-robot interaction, delicate manipulation and stable grasping, from left 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < to right and top to bottom ( [21], [22], [23], [24], [25]- [27], [28], [29]). (Bottom) A range of visual sensors including cameras based on biological principles, as well as depth cameras, from left to right ( [30], [21]). Red boxes indicate the work that utilizes visual and tactile sensors for visual-tactile fusion dataset acquisition.…”

Section: Figmentioning

confidence: 99%

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Miao

Zhou

et al. 2022

IEEE Trans. Med. Robot. Bionics

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Humanoid robots are designed and expected to resemble humans in structure and behavior, showing increasing application potentials in various fields. Like their biological counterparts, their environmental perception ability is fundamental. In particular, the visual and tactile perception are the two main sensory modes that humanoids use to understand and interact with the environment. Vision-Tactile Fusion Perception (VTFP) has shown multiple possibilities for better sensing understanding in challenging conditions, causing new research interests and questions. The overlap between visual and tactile perception in humanoids is continually growing. This work has reviewed the current state of the art of VTFP. It starts with the physiological basis of biological vision and tactile systems as well as the VTFP mechanisms as inspirations for humanoid perception. Then, the bioinspired visual-tactile fusion systems for humanoids are reviewed as the emphasis. After the survey on the vision and tactile sensors of robots, seven currently publicly available VTFP datasets are introduced. They are the data sources for several studies on neural network-inspired fusion algorithms. Furthermore, the applications of VTFP on humanoids are summarized. Finally, the challenges and future work are discussed. This review aims to provide several references for further exploitation of VTFP and its applications on humanoids.

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Section: Figmentioning

confidence: 99%

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Miao

Zhou

et al. 2022

IEEE Trans. Med. Robot. Bionics

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“…The sensor used in this work is the dynamic vision sensor (DVS)specifically, the DVS128-which is the first commercially available event-based camera. 33…”

Section: Event-based Camerasmentioning

confidence: 99%

“…The sensor used in this work is the Dynamic Vision Sensor (DVS) -specifically, the DVS128 -which is the first commercially available event-based camera [33]. It features a 128x128 pixel grid operating at an intrascene dynamic range of 120 dB, measuring events at 1 µs timing resolution with a latency of 15 µs [32].…”

Section: B Event-based Camerasmentioning

confidence: 99%

Vertical landing for micro air vehicles using event‐based optical flow

Hordijk

Scheper

Croon

2017

Journal of Field Robotics

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Small flying robots can perform landing maneuvers using bio‐inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images onboard, which limits the update rate of divergence measurements and thus the speed of the control loop and the robot. Event‐based cameras overcome these limitations by only measuring pixel‐level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This paper presents, to the best of our knowledge, the first work integrating event‐based optical flow estimation into the control loop of a flying robot. We extend an existing “local plane fitting” algorithm to obtain an improved and more computationally efficient optical flow estimation method, which is valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event‐based optical flow is introduced that accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller onboard a quadrotor. Experiments show that, using event‐based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.

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“…Event cameras are neuromorphic visual sensors inspired by the biological retina, and work in a completely different way from frame-based cameras. They use an address-event representations (AER) and output pixel-level brightness changes (called events) with microsecond resolution to generate sparse asynchronous event streams [6] [7] [8] [9]. Event cameras have the advantages of low latency, high temporal resolution, low bandwidth, low power consumption and high dynamic range, which can effectively overcome the problems existing in typical frame-based cameras.…”

Section: Introductionmentioning

confidence: 99%

Event-VPR: End-to-End Weakly Supervised Deep Network Architecture for Visual Place Recognition Using Event-Based Vision Sensor

Kong

Fang

Hou

et al. 2022

IEEE Trans. Instrum. Meas.

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Traditional visual place recognition (VPR) methods generally use frame-based cameras, which will easily fail due to rapid illumination changes or fast motion. To overcome this, we propose an end-to-end visual place recognition network using event cameras, which can achieve good recognition performance in challenging environments (e.g., large-scale driving scenes). The key idea of the proposed algorithm is firstly to characterize the event streams with the EST voxel grid representation, then extract features using a deep residual network, and finally aggregate features using an improved VLAD network to realize end-to-end visual place recognition using event streams. To verify the effectiveness of the proposed algorithm, on the event-based driving datasets (MVSEC, DDD17, Brisbane-Event-VPR) and the synthetic event datasets (Oxford RobotCar, CARLA), we analyze the performance of our proposed method on large-scale driving sequences including cross-weather, cross-season and illumination changing scenes, and then we compare the proposed method with state-of-the-art event-based VPR method (Ensemble-Event-VPR) to prove its advantages. Experimental results show that the performance of the proposed method is better than that of event-based ensemble scheme in challenging scenarios. To our knowledge, for visual place recognition task, this is the first endto-end weakly supervised deep network architecture that directly processes event stream data.

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A Review of Bioinspired Vision Sensors and Their Applications

Abstract: The aim of this short review is to provide some background information as well as an overview of a range of modern optical storage phosphor materials and their applications in dosimetry, computed radiography, and radiation therapy.

Cited by 12 publications

References 0 publications

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Vertical landing for micro air vehicles using event‐based optical flow

Event-VPR: End-to-End Weakly Supervised Deep Network Architecture for Visual Place Recognition Using Event-Based Vision Sensor

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