Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

TL;DR

This paper presents a night-time obstacle detection and avoidance system for unmanned vehicles using bio-inspired event-based vision sensors, which outperform traditional cameras in low-light conditions.

Contribution

It introduces a novel obstacle detection and avoidance framework leveraging event-based cameras, robust filtering, Hough transform, and triangulation for night-time autonomous navigation.

Findings

Event-based cameras outperform traditional cameras in low-light obstacle detection.

The proposed system effectively filters noise and detects obstacles in night conditions.

Qualitative results show improved detection accuracy using event sensors.

Abstract

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.