# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
###### Abstract  
Real-time stereo visual odometry (SVO) localization is a challenging problem, especially for a mobile platform without parallel computing capability. A possible solution is to reduce the computational complexity of SVO using a Kanade-Lucas-Tomasi (KLT) feature tracker. However, the standard KLT is susceptible to scale distortion and affine transformation. Therefore, this work presents a novel SVO algorithm yielding robust and real-time localization based on an improved KLT method. First, in order to improve real-time performance, feature inheritance is applied to avoid time-consuming feature detection and matching processes as much as possible. Furthermore, a joint adaptive function with respect to the average disparity, translation velocity, and yaw angle is proposed to determine a suitable window size for the adaptive KLT tracker. Then, combining the standard KLT method with an epipolar constraint, a simplified KLT matcher is introduced to substitute feature-based stereo matching. Additionally, an effective veer chain matching scheme is employed to reduce the drift error. Comparative experiments on the KITTI odometry benchmark show that the proposed method achieves significant improvement in terms of time performance than the state-of-the-art single-thread approaches and strikes a better trade-off between efficiency and accuracy than the parallel SVO or multi-threaded SLAM. stereo visual odometry; feature inheritance; adaptive KLT tracker; veer chain matching +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]. Real-Time Stereo Visual Odometry Based on an Improved KLT Method. _Appl. Sci._** 2022, 12, 12124. [[https://doi.org/10.3390/app122312124](https://doi.org/10.3390/app122312124)]([https://doi.org/10.3390/app122312124](https://doi.org/10.3390/app122312124))  
doi:10.3390/app122312124  
Academic Editors [PERSON] and [PERSON]  
Received: 26 October 2022  
Accepted: 24 November 2022  
Published: 27 November 2022  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. such as an inertial measurement unit and wheel encoder-based odometry [8; 9; 10], the range of searching for features and matching can be greatly reduced. However, this method is prone to a large error accumulation, especially for vehicles losing traction on large rocks and steep slopes [9; 10]. Another common solution is parallelization through multithread programming [11], FPGA, or GPU acceleration [12; 13]. The four-thread architecture of OV\({}^{2}\)SLAM [14] can run at 200 Hz on a 3.0 GHz CPU with eight cores. A real-time SVO that relies on heavy parallelism can limit its applications in mobile vehicles [7]. Therefore, the main purpose of this work was to develop a single-thread SVO without any prior knowledge of the motion to produce real-time and robust localization on a standard CPU. According to the publicly available KITTI leaderboard, the fast and robust visual odometry (FRVO) in [6] exceeds all other validated methods in real-time performance. In their implementation, a pruning corner detector and an improved Kanade-Lucas-Tomasi (KLT) tracker are able to reduce the computational complexity of SVO. The speed of FRVO is 0.03 s per frame on a 3.5 GHz CPU, with an average translation error of 1.26% and a rotation error of 0.0038 deg/m. However, to determine a suitable window size for the KLT tracker, FRVO requires a dense disparity map to be provided beforehand and the abovementioned time performance does not include the time for a dense disparity computation, which is usually a time-consuming process. In this paper, the proposed approach achieves better real-time performance on a lower-speed processor with similar localization accuracy. Instead of the three improvements mentioned above, a new approach is proposed to reduce computational complexity through both feature inheritance (FI) and an improved KLT method. The KLT improvements include an adaptive KLT tracker (AKT) and a simplified KLT matcher (SKM).