In this paper, a novel approach is presented to estimate the metric scale (MSC) and roll and pitch angles of a platform by using distance sensors in a monocular visual odometry setup. A state-of-the-art visual odometry algorithm Semi-Direct Visual Odometry (SVO) [1] is used to obtain sparse three dimensional (3D) point cloud which is then matched with the measurements obtained from the distance sensors for the estimation process. Metric scale with Kalman (MSCwK) filter approach is presented where the metric scale parameter is modeled as a Gaussian random variable and updated with a Kalman filter to improve robustness and accuracy. Maximum Likelihood (ML) method is presented to include multiple distance sensors for a better metric scale estimation. The estimation of the roll and pitch angles for the camera platform is considered. This is achieved with respect to the ground plane using at least three distance sensors placed in a specific geometry to overcome ambiguity and obtain a unique solution. Proposed approach can handle terrain irregularities and does not have drift. Several simulations are performed and the performances of the proposed approaches are compared with the previous works and SVO. The experiments also include real data to show the practical relevance. It is shown that the proposed approaches improve both the metric scale and roll and pitch angles significantly.

在本文中，提出了一种通过在单目视觉里程计设置中使用距离传感器来估计平台的公制尺度 (MSC) 以及滚动和俯仰角的新方法。最先进的视觉里程计算法半直接视觉里程计 (SVO) [1] 用于获得稀疏的三维 (3D) 点云，然后将其与从距离传感器获得的测量值进行匹配以进行估计过程. 提出了使用卡尔曼 (MSCwK) 滤波器的度量尺度方法，其中度量尺度参数被建模为高斯随机变量，并使用卡尔曼滤波器进行更新，以提高鲁棒性和准确性。提出了最大似然 (ML) 方法以包括多个距离传感器，以实现更好的度量尺度估计。考虑了相机平台的滚转角和俯仰角的估计。这是相对于地平面使用至少三个放置在特定几何形状中的距离传感器来实现的，以克服歧义并获得独特的解决方案。提议的方法可以处理地形不规则并且没有漂移。进行了几次模拟，并将所提出方法的性能与以前的工作和 SVO 进行了比较。实验还包括真实数据以显示实际相关性。结果表明，所提出的方法显着改善了度量尺度以及滚转和俯仰角。进行了几次模拟，并将所提出方法的性能与以前的工作和 SVO 进行了比较。实验还包括真实数据以显示实际相关性。结果表明，所提出的方法显着改善了度量尺度以及滚转和俯仰角。进行了几次模拟，并将所提出方法的性能与以前的工作和 SVO 进行了比较。实验还包括真实数据以显示实际相关性。结果表明，所提出的方法显着改善了度量尺度以及滚动和俯仰角。