The autonomous real-time optical navigation of planetary UAV is of the key technologies to ensure the success of the exploration. In such a GPS denied environment, vision-based localization is an optimal approach. In this paper, we proposed a multi-modal registration based SLAM algorithm, which estimates the location of a planet UAV using a nadir view camera on the UAV compared with pre-existing digital terrain model. To overcome the scale and appearance difference between on-board UAV images and pre-installed digital terrain model, a theoretical model is proposed to prove that topographic features of UAV image and DEM can be correlated in frequency domain via cross power spectrum. To provide the six-DOF of the UAV, we also developed an optimization approach which fuses the geo-referencing result into a SLAM system via LBA (Local Bundle Adjustment) to achieve robust and accurate vision-based navigation even in featureless planetary areas. To test the robustness and effectiveness of the proposed localization algorithm, a new cross-source drone-based localization dataset for planetary exploration is proposed. The proposed dataset includes 40200 synthetic drone images taken from nine planetary scenes with related DEM query images. Comparison experiments carried out demonstrate that over the flight distance of 33.8km, the proposed method achieved average localization error of 0.45 meters, compared to 1.31 meters by ORB-SLAM, with the processing speed of 12hz which will ensure a real-time performance. We will make our datasets available to encourage further work on this promising topic.
翻译:行星无人驾驶航空器自主实时光学导航是确保探测成功的关键技术。在这种被否定的全球定位系统环境中,基于愿景的本地化是一种最佳办法。在本文件中,我们提议采用基于SLAM的多模式注册算法,该算法使用无人驾驶航空器上的天文摄像头来估计行星无人驾驶航空器的位置,与先前存在的数字地形模型相比,对无人驾驶航空器上无人驾驶航空器的自动实时光学导航器与先前存在的数字地形模型进行比较;为了克服无人驾驶航空器图像与预先安装的数字地形模型之间的规模和外观差异,提议了一个理论模型,以证明无人驾驶航空器图像和德国马克的地形特征可以通过跨电频谱在频域中发生关联。为提供无人驾驶航空器的六度DOF,我们还制定了一种优化方法,将地理定位结果通过LBA(本地布德勒调整)连接到SALM系统,以稳妥和准确的视觉导航。为了测试拟议的本地化算法的新的跨源无人驾驶飞行器定位定位数据集,包括40-200个合成无人驾驶飞行器图像,从九度SLSLAF 5,将比AMA的飞行平地平面图像与拟议的12度图像进行比较。