Improving Keypoints Tracking With Machine-Learned Features in Event-Camera-Based Visual Odometry

This paper introduces a feature detection method developed through machine learning specifically tailored for event-camera based visual odometry techniques used in reconstructing trajectories for unmanned aerial vehicles. The proposed approach leverages machine-learned features to improve the precis...

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Bibliographic Details
Main Authors: Chiodini, Sebastiano, Trevisanuto, Giovanni, Bettanini, Carlo, Colombatti, Giacomo, Pertile, Marco
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Feature extraction
LiDAR
Navigation
Robot vision systems
Robotics
SLAM
Tracking
Training
Trajectory
Visualization
Online Access:Request full text
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Summary:This paper introduces a feature detection method developed through machine learning specifically tailored for event-camera based visual odometry techniques used in reconstructing trajectories for unmanned aerial vehicles. The proposed approach leverages machine-learned features to improve the precision of trajectory reconstruction. Unlike conventional visual odometry methods, which often struggle in low light and high-speed scenarios, the event-camera-based method addresses these challenges by focusing solely on detecting and processing changes in the visual scene. The machine-learned features are designed to capture the distinctive attributes of event-camera data, thereby refining the accuracy of trajectory reconstruction. The inference pipeline consists of a module that is iterated twice sequentially, comprising a Squeeze-and-Excite block and a ConvLSTM block with residual connection. This is succeeded by a final convolutional layer that generates trajectory information for corners in the form of heatmap sequences. In the experimental phase, a series of images was gathered using an event-camera in outdoor settings for both training and testing purposes.
ISSN:2575-7490
DOI:10.1109/MetroAeroSpace61015.2024.10591575