论文标题
使用前馈神经网络的智能车辆单眼视觉探空仪的漂移减少
Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks
论文作者
论文摘要
在本文中，提出了一种基于进发神经网络的方法来减少单眼视觉探光算法漂移的方法。视觉轨道算法算法计算连续相机框架之间车辆的增量运动，然后集成这些增量以确定车辆的姿势。提出的神经网络减少了车辆的姿势估计中的误差，这是由于特征检测和匹配，摄像机内在参数等不准确而导致的。这些不准确性传播到对车辆的运动估计，从而导致大量估计误差。降低神经网络的漂移基于连续的摄像头框架中特征的运动来识别此类错误，从而导致更准确的增量运动估计。使用KITTI数据集对拟议的漂移减少神经网络进行了训练和验证，结果表明，所提出的方法在减少增量方向估计中的误差方面的疗效，从而减少了姿势估计中的整体误差。
In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural network reduces the errors in the pose estimation of the vehicle which results from the inaccuracies in features detection and matching, camera intrinsic parameters, and so on. These inaccuracies are propagated to the motion estimation of the vehicle causing larger amounts of estimation errors. The drift reducing neural network identifies such errors based on the motion of features in the successive camera frames leading to more accurate incremental motion estimates. The proposed drift reducing neural network is trained and validated using the KITTI dataset and the results show the efficacy of the proposed approach in reducing the errors in the incremental orientation estimation, thus reducing the overall error in the pose estimation.
