近年来,视觉里程计广泛应用于机器人和自动驾驶等领域,传统方法求解视觉里程计需基于特征提取、特征匹配和相机校准等复杂过程,同时各个模块之间要耦合在一起才能达到较好的效果,且算法的复杂度较高。环境噪声的干扰以及传感器的精度会影响传统算法的特征提取精度,进而影响视觉里程计的估算精度。鉴于此,提出一种基于深度学习并融合注意力机制的视觉里程计算法,该算法可以舍弃传统算法复杂的操作过程。实验结果表明,所提算法可以实时地估计相机里程计,并具有较高的精度和稳定性以及较低的网络复杂度。

Recently, visual odometry has been widely used in robotics and autonomous driving. Traditional methods for addressing visual odometry are based on complex processes such as feature extraction, feature matching, and camera calibration. Moreover, each module must be integrated to achieve improved results, and the algorithm is high complexity. The interference of environmental noise and the accuracy of the sensor affect the feature extraction accuracy of the traditional algorithm, thereby affecting the estimation accuracy of the visual odometer. In this context, a visual mileage calculation method based on deep learning and fusion attention mechanism is proposed. The proposed method can eliminate the complicated operation process of traditional algorithms. Experimental results show that the proposed algorithm can estimate the camera odometer in real time achieves improved accuracy and stability and reduced network complexity.