Ellipse Crater Recognition for Lost-in-Space Scenario

Abstract

In the field of deep space exploration, a crater recognition algorithm is key to landing navigation based on craters. When there is only visual information, determining how to identify the crater and provide the initial pose of the lander for a lost-in-space (LIS) scenario is a difficulty in terrain relative navigation (TRN). In this paper, a fast and robust crater recognition method for absolute pose estimation based on projective invariants is proposed, which can provide an accurate initial pose for tracking navigation. First, the method selects navigation craters to establish a small-capacity and high-efficiency crater database, and crater pair serial numbers and projective invariants are stored. Second, our method uses a dynamic threshold to solve the problem that the projective invariants are sensitive to noise. Then, an iterative pyramid algorithm is proposed to quickly filter redundancies. Using a dynamic threshold, the matching rate was increased by at least 10%, and the average processing speed was increased by 40%. When the detection errors of the major and minor axes of the ellipse reached 5%, the detection error of the center point reached 1 pixel, and the tilt angle error reached 5°; the matching rate was still >80%. Finally, the pose was estimated by solving the perspective-n-point (PNP) problem based on the recognized craters. The initial pose error in the simulation environment was less than 2°, and the position error was less than 44 m.