This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone.
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机译:本文提出了一种新颖的方法,该方法使用紧密耦合的惯性和视觉传感器估算在动态和未知环境中车辆的自我运动。为了提高准确性和鲁棒性,我们利用点和线要素的组合来辅助导航。该数学框架基于图像三胞胎之间的三焦点几何,这对于点和线特征而言是简单且统一的。对于融合算法设计,我们采用扩展卡尔曼滤波器(EKF)进行错误状态预测和协方差传播,并采用Sigma Point Kalman滤波器(SPKF)进行鲁棒的测量更新,以解决高非线性问题。室外和室内实验表明,与仅使用点特征的算法相比,点和线特征的组合提高了估计的准确性和鲁棒性。
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