We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate the excellent performance of AstroSLAM using both real legacy mission imagery and trajectory data courtesy of NASA's Planetary Data System, as well as real in-lab imagery data generated on a 3 degree-of-freedom spacecraft simulator test-bed.

翻译：我们提议对一个未知目标小天体的自主在线导航采用独立、基于愿景的AstroSLAM,这是针对一个未知目标小天体的自主在线导航的一种独立解决方案。AstroSLAM的前提是将SLAM问题设计成一个递增式要素图,由GTSAM图书馆和iSAM2引擎的利用加以促进。通过将传感器与轨道运动前导体相结合,我们取得了比基线SLAM解决方案更好的性能。我们将轨道动作限制纳入系数图中,设计了一个新的相对动态系数,将航天器的相对构成与航天器运动在小天体附近产生的轨迹预测问题联系起来。我们展示了AstroSLAM的出色性能,使用了美国航天局行星数据系统的真实遗留任务图像和轨迹数据,以及从3度自由航天器模拟试验台生成的实在实验室中的图像数据。