Trajectory reconstruction by means of an event-camera-based visual odometry method and machine learned features
S. Chiodini, G. Trevisanuto, C. Bettanini, G. Colombatti, M. Pertiledownload PDF
Abstract. This paper presents a machine learned feature detector targeted to event-camera based visual odometry methods for unmanned aerial vehicles trajectory reconstruction. The proposed method uses machine-learned features to enhance the accuracy of the trajectory reconstruction. Traditional visual odometry methods suffer from poor performance in low light conditions and high-speed motion. The event-camera-based approach overcomes these limitations by detecting and processing only the changes in the visual scene. The machine-learned features are crafted to capture the unique characteristics of the event-camera data, enhancing the accuracy of the trajectory reconstruction. The inference pipeline is composed of a module repeated twice in sequence, formed by a Squeeze-and-Excite block and a ConvLSTM block with residual connection; it is followed by a final convolutional layer that provides the trajectories of the corners as a sequence of heatmaps. In the experimental part, a sequence of images was collected using an event-camera in outdoor environments for training and test.
Visual Odometry, Computer Vision, Machine Learning
Published online 11/1/2023, 4 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: S. Chiodini, G. Trevisanuto, C. Bettanini, G. Colombatti, M. Pertile, Trajectory reconstruction by means of an event-camera-based visual odometry method and machine learned features, Materials Research Proceedings, Vol. 37, pp 705-708, 2023
The article was published as article 150 of the book Aeronautics and Astronautics
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