# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 1 Introduction  
Simultaneous localization and mapping (SLAM) technology has found widespread application in numerous domains, such as autonomous driving, robotics, and augmented reality. Visual SLAM techniques play a significant role in this field and have garnered considerable attention from both the computer vision (CV) and robotics communities over the past few decades, as they are based on a low-cost and small sensor system [1; 2], which guarantees these advantages compared to other sensor-based SLAM techniques. As an essential module of a visual SLAM system, VO aims to accurately estimate camera motion from frame sequences.  
In recent decades, there has been a significant amount of research focused on creating precise, dependable, and robust VO methods. Typically, these methods fall into two main categories, VO based on geometry and VO based on learning. In geometry-based VO approaches, the final outcome is achieved by using a coarse-to-fine iteration procedure [3; 4]. With careful initialization or relocalization within the generated maps, these methods have demonstrated a remarkable level of accuracy. Geometry-based VO methods heavily rely on manual intervention to detect, analyze, and address failures; track faults; and refine localization results. Alternatively, learning-based VO methods leverage neural networks to extract features from entire RGB input frames and then regress the features to obtain the camera's ego-motion [5; 6; 7; 8]. Some of these methods incorporate intermediate parameters such as optical flow maps [9; 10; 11], while others use additional information such as semantic segmentation [12; 13]. Moreover, some treat VO as a subset of a larger multi-task framework [14; 15; 16; 17]. VO methods can be broadly classified into monocular and stereo VO based on the type of camera used. Monocular VO methods recover absolute scale byutilizing additional information [3; 4; 18], such as the height of the camera or labels. Stereo frames, with their depth information, provide an inherent scale reference. Our approach can effectively eliminate the initial operation required in ORB-SLAM2 [18] when working with stereo frames. Several methods [15; 16; 17; 19] estimate depth and ego-motion together, but they fail to account for the presence of moving objects, leading to suboptimal results. In this paper, our goal is to investigate the feasibility of estimating the frame-to-frame ego-motion of a camera solely from stereo image pairs.  
We introduce a state-of-the-art network that utilizes stereo image pairs to estimate camera ego-motion by utilizing optical flow and depth information as intermediate quantities. Optical flow enables the network to discern between static and moving objects, while depth information provides an absolute scale. The primary contributions of our study are as follows: (1) we present a new approach to learning VO that effectively leverages optical flow and depth information to estimate ego-motion; (2) we put forth a novel structural ssim cost volume (SSIMCV) for extracting depth information from stereo image pairs; and (3) our method achieves state-of-the-art results when compared to existing geometry-based and learning-based VO methods on the KITTI visual odometry dataset. The remainder of this paper is structured as follows: SS2 discusses relevant VO studies, SS3 provides an overview of our StereoVO architecture, SS4 compares the performance of our approach to other state-of-the-art methods, and SS5 concludes the paper.