论文标题
基于生物启发的视觉传感器的夜视障碍物检测和回避
Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors
论文作者
论文摘要
朝着自主权迈进，无人驾驶汽车在很大程度上依赖于最新的避免碰撞系统（CAS）。但是，尤其是在夜间的障碍物发现仍然是一项艰巨的任务，因为照明条件不足以使传统摄像机正常运行。因此，我们利用基于事件的相机的强大属性来在低照明条件下执行障碍物检测。事件摄像机以高输出时间速率异步触发事件，高动态范围高达120美元$ db $。该算法使用可靠的Hough变换技术过滤背景活动噪声并提取对象。每个检测到的对象的深度是通过使用LC-Harris提取的三角剖分的2D特征来计算的。最后，采用异步自适应碰撞（AACA）算法进行有效避免。使用事件相机和传统相机比较定性评估。
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.
