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Related Experiment Video

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Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors.

Lukas Everding¹, Jörg Conradt¹

¹Department of Electrical and Computer Engineering, Neuroscientific Systemtheory, Technical University of Munich, Munich, Germany.

Frontiers in Neurorobotics

|March 9, 2018

Summary

This study introduces a new method for tracking line structures using event-based cameras (DVS). This approach offers continuous feature tracking for autonomous systems, improving navigation in dynamic environments.

Keywords:

event-based vision line detection line tracking low-level feature extraction neuromorphic sensors robotic vision silicon retina

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Area of Science:

Computer Vision
Robotics
Sensor Technology

Background:

Autonomous systems require robust visual feature extraction for navigation.
Traditional cameras with fixed sampling rates lose information between frames.
Event-based cameras (Dynamic Vision Sensors - DVS) offer quasicontinuous data streams.

Purpose of the Study:

To develop a novel method for detecting and tracking line structures from DVS data.
To leverage the high temporal resolution of DVS for continuous feature tracking.
To enable real-time, low-latency visual odometry for mobile robotics.

Main Methods:

Utilizing DVS address events generated by luminance changes.
Detecting planes of events in x-y-t space.
Tracing these event planes through time to track line structures.

Main Results:

The proposed method successfully tracks line structures in real-time.
The approach is robust against noise in DVS data.
Demonstrated efficacy on real-world datasets with artificial structures.

Conclusions:

The novel DVS-based line tracking method enhances feature tracking for autonomous systems.
Continuous tracking capabilities are suitable for low-latency robotic applications.
This method addresses limitations of frame-based cameras in feature tracking.