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Piotr Dudek

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3 papers

1 author row

ICRA Conference 2020 Conference Paper

Proximity Estimation Using Vision Features Computed On Sensor

Jianing Chen 0005
Yanan Liu
Stephen J. Carey
Piotr Dudek

This paper presents a monocular vision based proximity estimation system using abstract features, such as corner points, blobs and edges, as inputs to a neural network. An experimental vehicle was built using a vision system integrating the SCAMP-5 vision chip, a micro-controller, and an RC model car. The vision chip includes image sensor with embedded 256×256 processor SIMD array. The pixel processor array chip was programmed to capture images and run the feature algorithms directly on the focal plane, and then digest them so that only sparse feature description data were read-out in the form of 40 values. By logging the vision output and the output from three infrared proximity sensors, training data were obtained to train three fully connected layer-recurrent neural networks with fewer than 700 parameters each. The trained neural network was able to estimate the proximity to the level of accuracy sufficient for a reactive collision avoidance behaviour to be achieved. The latency of the control system, from image capture to neural network output, was under 4ms, enabling the vehicles to avoid obstacles while moving at 0. 64m/s to 1. 8m/s in the experiment.

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IROS Conference 2018 Conference Paper

Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array

Colin Greatwood
Laurie Bose
Thomas Richardson 0002
Walterio W. Mayol-Cuevas
Jianing Chen 0005
Stephen J. Carey
Piotr Dudek

This paper presents a visual odometry approach using a Pixel Processor Array (PPA) camera, specifically, the SCAMP-5 vision chip. In this device, each pixel is capable of storing data and performing computation, enabling a variety of computer vision tasks to be carried out directly upon the sensor itself. In this work the PPA performs HDR edge detection, perspective correction and image alignment based odometry, allowing the position and heading of a MAV to be tracked at several hundred frames per second. We evaluate our PPA based approach by direct comparison with a motion capture system for a variety of trajectories. These include rapid accelerations that would incur significant motion blur at low frame rates, and lighting conditions that would typically lead to under or over exposure of image detail. Such challenging conditions would often lead to unusable images when relying on traditional image sensors.

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IROS Conference 2017 Conference Paper

Tracking control of a UAV with a parallel visual processor

Colin Greatwood
Laurie Bose
Thomas Richardson 0002
Walterio W. Mayol-Cuevas
Jianing Chen 0005
Stephen J. Carey
Piotr Dudek

This paper presents a vision-based control strategy for tracking a ground target using a novel vision sensor featuring a processor for each pixel element. This enables computer vision tasks to be carried out directly on the focal plane in a highly efficient manner rather than using a separate general purpose computer. The strategy enables a small, agile quadrotor Unmanned Air Vehicle (UAV) to track the target from close range using minimal computational effort and with low power consumption. To evaluate the system we target a vehicle driven by chaotic dual-pendulum trajectories. Target proximity and the large, unpredictable accelerations of the vehicle cause challenges for the UAV in keeping it within the downward facing camera's field of view (FoV). A state observer is used to smooth out predictions of the target's location and, importantly, estimate velocity. Experimental results also demonstrate that it is possible to continue to re-acquire and follow the target during short periods of loss in target visibility. The tracking algorithm exploits the parallel nature of the visual sensor, enabling high rate image processing ahead of any communication bottleneck with the UAV controller. With the vision chip carrying out the most intense visual information processing, it is computationally trivial to compute all of the controls for tracking onboard. This work is directed toward visual agile robots that are power efficient and that ferry only useful data around the information and control pathways.

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