# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
Recently, the application of Deep Neural Networks (DNNs) for the VO has been intensively investigated [22; 23; 24; 25; 26]. Most of the applications of DNNs is for learning-based VO methods. In [22], [PERSON] et al. presented a real-time re-localization system using Deep Convnet consisting of CNNs. Since VO is a continuation of sequential estimation, architectures such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU) network can be adopted. In [23], [PERSON] et al. proposed a learning-based Monocular Visual Odometry (MVO) with the help of deep Recurrent Convolutional Neural Network (RCNN). In [24], VO approach using a dual-stream neural network consisting of a CNN and an LSTM network is proposed by [PERSON] et al. [PERSON] et al. presented an MVO system employing RCNN [25]. In [26], [PERSON] et al. proposed Learning Kalman Network for improved learning of dynamically changing parameters during end-to-end training for VO without specifying parameters explicitly. In learning-based VO methods, structures of feature-based VO methods and direct VO methods are replaced with the fine-tuned DNNs, however, results of the KITTI benchmark test show that the performances of learning-based VO methods have not yet surpassed the performances of the classical VO (e.g., feature-based VO methods, direct VO methods). For this reason, hybrid methods making use of classical VO combined with learning-based VO are studied in [27; 28]. In [27], [PERSON] et al. proposed a Deep Pose Correction (DPC)  
Figure 1: The process of Visual Odometry (VO) methods: (**a**) feature-based VO method and (**b**) direct VO method [5]. network consisting of CNNs. The DPC network predicts camera motion correction according to stereo images. A classical VO framework is fused with the DPC network by Pose Graph Relaxation. In [28], pose estimation relying on the combined use of classical VO with DNNs is presented with camera motion estimated by pre-processed optical flow images. Despite foregoing efforts, many problems in the VO remain unsolved. Imaging conditions (e.g., sunlight, shadows, image blur) fundamentally affect the performance of the VO [4]. To overcome the problems that occurred by change of light source, various approaches have been attempted. For instance, learning-based detectors (e.g., R2D2, SuperPoint [11]) have proven to be superior to non-machine learning detectors (e.g., Speeded-Up Robust Features, Scale Invariant Feature Transform). When the camera rotates (e.g., yaw rotation), a large change of pixels in the image occurs as compared to that due to translation. The large change of pixels in the image quickly reduces the lifespan of the feature points when using feature-based VO methods, and also causes large rotation error and translation error in learning-based VO methods as well as classical VO [29; 30; 31; 32]. Furthermore, the learning-based VO methods require tremendous time for learning, due to its usage of images to train the network. This work introduces a correction method for the VO by using prior driving data generated from a relatively accurate sensor such as Real-Time Kinematic Global Positioning System (RTK-GPS) which has 0.02 m/0.1 deg resolution [33]. The performance of the correction method is not affected by the characteristics of images. Being inspired by the trend of using a learning-based VO and classical VO together, this work presents a pose estimation that uses a GRU network which is an improved version of RNN and computationally more efficient than the LSTM network [34; 35]. The GRU network is trained by historical trajectory data of the yaw angle constrained by the shape and structure of a robot or a vehicle. The GRU network receives the stacked yaw angles converted from 6 Degrees of Freedom (DoF) as the input, which is the result of classical VO, and predicts the next yaw angle. The 2 yaw angles, one obtained from the GRU network and the other obtained from the classical VO, are fused together. The simulation is conducted with a VO adopting a MVO framework.