融合事件的点线特征法视觉惯性里程计

Event-combined Visual-inertial Odometry Using Point and Line Features

视觉惯性里程计是机器人实现自主定位的关键技术,事件相机作为一种异步视觉传感器,与传统相机具有互补的特点。针对低光照、光照大幅度变化和高速运动场景,对事件相机的输出和传统图像进行融合,并结合惯性测量单元进行实时点线特征法视觉惯性里程计研究。提出一种从事件流生成事件图像的算法,设计融合事件的点线特征检测方法;基于视觉-惯性紧耦合的思想,设计后端滑动窗口优化算法;进行数据集试验验证和无人机飞行试验验证。在数据集上的试验结果表明:与仅使用传统图像的点线特征法视觉惯性里程计相比,在高速运动的场景下,定位误差平均减少了22%以上;在低光照、光照大幅度变化的场景下,定位误差平均减少了59%以上。

Visual-inertial odometry is a key technology for robots to achieve autonomous localization.As an asynchronous vision sensor,the event cameras have complementary to the traditional cameras.For the scene of low light condition,high dynamic range and high-speed motion,the output of event camera and the traditional image are fused.A real-time visual inertial odometry using point and line features is proposed combined with the inertial measurement unit(IMU).An algorithm for generating an event image from event stream is proposed,a point-line feature detection method combined with events is designed,anda back-end sliding window optimization algorithm is designed based on the idea of visual-inertial tight-coupling.The dataset test and UAV flight test are conducted.The test results on the dataset show that,compared with the visual-inertial odometry using point and line features only on the traditional image,the proposed odometry can reduce the positioning error by more than 22%on average in the scene of high-speed motion,and it can reducethe positioning error by more than 59%on average in the scene of low light condition and high dynamic range.