Autonomous robotics is critically affected by the robustness of its scene understanding algorithms. We propose a two-axis pipeline based on polarization indices to analyze dynamic urban scenes. As robots evolve in unknown environments, they are prone to encountering specular obstacles. Usually, specular phenomena are rarely taken into account by algorithms which causes misinterpretations and erroneous estimates. By exploiting all the light properties, systems can greatly increase their robustness to events. In addition to the conventional photometric characteristics, we propose to include polarization sensing. We demonstrate in this paper that the contribution of polarization measurement increases both the performances of segmentation and the quality of depth estimation. Our polarimetry-based approaches are compared here with other state-of-the-art RGB-centric methods showing interest of using polarization imaging.

翻译：自主机器人受到其现场理解算法强健度的严重影响。 我们提出基于两极分化指数的双轴管道,以分析动态城市景象。 随着机器人在未知环境中的演化,他们很容易遇到视觉障碍。 通常,引起错误解释和错误估计的算法很少考虑到光学现象。 通过利用所有光特性,系统可以大大提高其对事件的强度。 除了传统的光度特征外,我们提议包括两极分化感测。 我们在本文件中表明,两极分化测量的贡献提高了分化的性能和深度估测的质量。 我们基于对地测量的方法在这里与其他最先进的 RGB 中心方法相比,这些方法显示了使用极化成像的兴趣。