Horizon based orientation estimation for planetary surface navigation

Abstract

Planetary rovers navigate in extreme environments for which a Global Positioning System (GPS) is unavailable, maps are restricted to relatively low resolution provided by orbital imagery, and compass information is often lacking due to weak or not existent magnetic fields. However, an accurate rover localization is particularly important to achieve the mission success by reaching the science targets, avoiding negative obstacles visible only in orbital maps, and maintaining good communication connections with ground. This paper describes a horizon solution for precise rover orientation estimation. The detected horizon in imagery provided by the on board navigation cameras is matched with the horizon rendered over the existing terrain model. The set of rotation parameters (roll, pitch yaw) that minimize the cost function between the two horizon curves corresponds to the rover estimated pose.

Horizon Line Stability Observations over the Sea

“…The local vertical estimation precision of such sensors is not very high, being (Gupta and Brennan, 2008). The planets or other solar system bodies that have a weak atmosphere are well-suited for horizon-based navigation (Cozman et al, 2000; Oiri et al, 2010; Nefian et al, 2014; Bouyssounouse et al, 2016; Cozman and Krotkov, 1996).…”

Experimental research of precise terrain occlusion prediction algorithm based on image sequences of the Chang’E-4 lunar rover

“…Cozman等人 [5] 利用行星巡视器相机图 像、姿态角信息和轨道器影像, 提出基于概率框架模 型的匹配方法, 获得巡视器高精度的位置与姿态角信 息. Oiri等人 [6] 利用行星巡视器全景相机图像和DEM 的匹配, 获得巡视器的全局位置. Ahmad等人 [7] 、 Bouyssounouse等人 [8] 、Nefian等人 [9] 提出了利用行星 月球车图像边缘信息和局部特征点提取天际线(地形 与天空的交界线)的方法, 然后与DEM图像中的虚拟天 际线进行配准, 实现无GPS或路标点信息的月球车定 位定向. Ahmad等人 [7] 提出利用边缘图像和局部特征 匹配点(SIFT匹配像点)等信息, 然后利用机器学习方 法对全景图像进行水平天际线的检测.…”

“…Ahmad等人 [7] 提出利用边缘图像和局部特征 匹配点(SIFT匹配像点)等信息, 然后利用机器学习方 法对全景图像进行水平天际线的检测. Bouyssounouse 等人 [8] 通过构建行星巡视器全景图像和DEM图像中天 际线的匹配最优化函数, 实现巡视器的姿态角的全局 计算. Nefian等人 [9] 利用视觉里程计、DEM和天际线 匹配的综合结果, 实现行星巡视全局位置与姿态角的 预估.…”