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.

Self-supervised monocular visual odometry based on cross-correlation

“…DSO [36] combines direct methods and sparse reconstruction to extract the highest intensity points in image blocks, using gradient intensity for pixel sampling, and obtaining camera poses through joint optimization. Guo et al [37] combined the standard KLT method with extreme outer constraints to introduce a simplified KLT matcher as an alternative to feature-based stereo matching, achieving a better trade-off between efficiency and accuracy. DynPL-SVO [38] improves the localization accuracy in dynamic scenes by introducing a dynamic network algorithm and designing a joint cost function.…”

A Lightweight Visual Odometry Based on LK Optical Flow Tracking

“…Moreover, the image is non-convex, so it is necessary to provide a reliable initial value for the optimization algorithm to avoid its non-convergence [26,27]. Guo et al [28] combine the standard KLT method with an epipolar constraint to substitute feature-based stereo matching, which achieved a better trade-off between efficiency and accuracy. Duo-VIO [29] applies a small baseline stereo camera with IMU, integrated feature observations, and IMU measurements obtained from the camera images to estimate the orientation, location, and velocity of the camera, it allows the system to execute pose estimation more rapidly and accurately.…”