Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors
This addresses a critical safety problem for autonomous vehicles in low-light environments, but it is incremental as it builds on existing event-camera and algorithm techniques.
The paper tackled obstacle detection and avoidance for unmanned vehicles at night by using event-based cameras, achieving effective performance in low lighting conditions with high dynamic range up to 120 dB.
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.