In this paper, we introduce an innovative approach for extracting trajectories from a camera sensor in GPS-denied environments, leveraging visual odometry. The system takes video footage captured by a forward-facing camera mounted on a vehicle as input, with the output being a chain code representing the camera's trajectory. The proposed methodology involves several key steps. Firstly, we employ phase correlation between consecutive frames of the video to extract essential information. Subsequently, we introduce a novel chain code method termed "dynamic chain code," which is based on the x-shift values derived from the phase correlation. The third step involves determining directional changes (forward, left, right) by establishing thresholds and extracting the corresponding chain code. This extracted code is then stored in a buffer for further processing. Notably, our system outperforms traditional methods reliant on spatial features, exhibiting greater speed and robustness in noisy environments. Importantly, our approach operates without external camera calibration information. Moreover, by incorporating visual odometry, our system enhances its accuracy in estimating camera motion, providing a more comprehensive understanding of trajectory dynamics. Finally, the system culminates in the visualization of the normalized camera motion trajectory.

翻译：本文提出了一种在无GPS环境下利用视觉里程计从摄像头传感器提取轨迹的创新方法。系统以车载前视摄像头捕获的视频作为输入，输出代表摄像头轨迹的链码。所提方法包含以下关键步骤：首先，利用连续视频帧间的相位相关性提取关键信息；其次，提出一种基于相位相关x位移值的新型链码方法——"动态链码"；第三步通过设定阈值判别方向变化（前进/左转/右转）并提取对应链码，将提取的链码存入缓冲区作进一步处理。值得注意的是，本系统相较于依赖空间特征的传统方法，在噪声环境下展现出更快的处理速度和更强的鲁棒性。最重要的是，本方法无需外部相机标定信息。通过融合视觉里程计，系统进一步提升了摄像头运动估计的精度，实现了对轨迹动态特性的更全面理解。最终，系统以标准化形式完成摄像头运动轨迹的可视化呈现。