Feature-based visual odometry with fusion of optical flow method in weak texture environment

Abstract

Abstract Feature-based visual odometry has difficulty in feature extraction and matching in weak texture environment, resulting in substantial inter-frame pose resolution errors. Meanwhile, the computation and matching of feature point descriptors can be time-consuming and computationally inefficient. To address these issues encountered by traditional ORB-SLAM odometry in texture-lacking regions, an enhanced method for visual odometry estimation is proposed. First, the quadtree technique is employed to extract ORB feature points with a uniform distribution and an adequate number. Subsequently, when processing non-critical frames, the optical flow method is utilized to predict the precise locations of the feature points, circumventing the need for feature matching. Following this, the random sampling consistency method is applied to eliminate mismatched points in optical flow tracking, ensuring that only high-quality internal points are retained. Afterwards, a system of nonlinear equations is solved using AP3P method to estimate the precise position of the camera. Finally, the trajectory is optimized by Dogleg algorithm to achieve accurate and stable tracking and positioning. The experimental results demonstrate that the improved algorithm outperforms mainstream ORB-SLAM3 algorithm in terms of operation efficiency and positioning accuracy across multiple experimental scenarios. This method effectively addresses the challenges of low tracking accuracy and poor real-time performance commonly encountered by traditional visual odometers operating in weak texture environments. As a result, the method combining the feature-based method and the optical flow method significantly enhances the application of visual odometry in complex environments by improving the tracking stability, motion estimation accuracy, and real-time performance.