We introduce a new real-time pipeline for Simultaneous Localization and Mapping (SLAM) and Visual Inertial Odometry (VIO) in the context of planetary rovers. We leverage prior information of the location of the lander to propose an object-level SLAM approach that optimizes pose and shape of the lander together with camera trajectories of the rover. As a further refinement step, we propose to use techniques of interpolation between adjacent temporal samples; videlicet synthesizing non-existing images to improve the overall accuracy of the system. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.
翻译:我们引入了新的实时管道,用于行星流流体中的同步定位和绘图(SLAM)和视觉惯性测量(VIO),我们利用着陆器位置的事先信息,提出一个目标水平的SLAM方法,优化着陆器的外形和形状,同时利用巡航器的相机轨迹,作为进一步的改进步骤,我们提议使用相邻时间样本之间的内插技术;将不存在的图像综合在一起,以提高系统的总体准确性。实验是在Iris LunarnerRover的背景下进行的,该纳米流体将于2021年作为卡内基·梅隆的旗舰部署在月球地形上,是美国第一个登月球的无人巡航。