Federal Institute of Rio Grande do Norte, Parnamirim 59143-455, Brazil.
Department of Electrical and Computer Engineering, University of São Paulo, São Carlos 13566-590, Brazil.
Sensors (Basel). 2023 Jan 26;23(3):1393. doi: 10.3390/s23031393.
Nowadays, state-of-the-art direct visual odometry (VO) methods essentially rely on points to estimate the pose of the camera and reconstruct the environment. Direct Sparse Odometry (DSO) became the standard technique and many approaches have been developed from it. However, only recently, two monocular plane-based DSOs have been presented. The first one uses a learning-based plane estimator to generate coarse planes as input for optimization. When these coarse estimates are too far from the minimum, the optimization may fail. Thus, the entire system result is dependent on the quality of the plane predictions and restricted to the training data domain. The second one only detects planes in vertical and horizontal orientation as being more adequate to structured environments. To the best of our knowledge, we propose the first Stereo Plane-based VO inspired by the DSO framework. Differing from the above-mentioned methods, our approach purely uses planes as features in the sliding window optimization and uses a dual quaternion as pose parameterization. The conducted experiments showed that our method presents a similar performance to Stereo DSO, a point-based approach.
如今，最先进的直接视觉里程计（VO）方法本质上依赖于点来估计相机的姿态并重建环境。直接稀疏里程计（DSO）成为了标准技术，并且从它衍生出了许多方法。然而，直到最近，才有两种基于单目平面的 DSO 被提出。第一种方法使用基于学习的平面估计器生成粗平面作为优化的输入。当这些粗略的估计离最小值太远时，优化可能会失败。因此，整个系统的结果取决于平面预测的质量，并受限于训练数据的范围。第二种方法仅检测垂直和水平方向的平面，因为它们更适合结构化环境。据我们所知，我们提出了第一个基于立体平面的 VO，它受到了 DSO 框架的启发。与上述方法不同，我们的方法在滑动窗口优化中纯粹使用平面作为特征，并使用对偶四元数作为姿态参数化。进行的实验表明，我们的方法与基于点的 Stereo DSO 具有相似的性能。
