We propose a novel approach for fast and accurate stereo visual Simultaneous Localization and Mapping (SLAM) independent of feature detection and matching. We extend monocular Direct Sparse Odometry (DSO) to a stereo system by optimizing the scale of the 3D points to minimize photometric error for the stereo configuration, which yields a computationally efficient and robust method compared to conventional stereo matching. We further extend it to a full SLAM system with loop closure to reduce accumulated errors. With the assumption of forward camera motion, we imitate a LiDAR scan using the 3D points obtained from the visual odometry and adapt a LiDAR descriptor for place recognition to facilitate more efficient detection of loop closures. Afterward, we estimate the relative pose using direct alignment by minimizing the photometric error for potential loop closures. Optionally, further improvement over direct alignment is achieved by using the Iterative Closest Point (ICP) algorithm. Lastly, we optimize a pose graph to improve SLAM accuracy globally. By avoiding feature detection or matching in our SLAM system, we ensure high computational efficiency and robustness. Thorough experimental validations on public datasets demonstrate its effectiveness compared to the state-of-the-art approaches.
翻译:我们提出一种与特征探测和匹配无关的快速和准确的立体视觉同步同步同步本地化和绘图新颖方法。我们通过优化3D点的规模,将立体配置的光度差差最小化,从而最大限度地减少立体配置的立体测量误差,从而产生一种与常规立体匹配相比的计算高效和稳健的方法。我们进一步将其扩展为具有循环关闭的完整SLM系统,以减少累积错误。通过假设前摄像机运动,我们用从视觉odorics获得的3D点模拟LIDAR扫描,并调整LIDAR描述仪,以进行地点识别,以便于更有效地探测环关闭。之后,我们通过尽可能减少光度差差分差来估计相对构成,通过使用隐性近点(ICP)算法来进一步改进直接匹配。最后,我们优化一个配置图表,以提高全球SLM的准确性。通过避免地貌探测或匹配我们的SLM系统,我们确保高计算效率和稳健度。在公共数据设置上的索罗夫实验性验证方法显示其有效性。