Abstract
A crater detection and recognition algorithm is the key to pose estimation based on craters. Due to the changing viewing angle and varying height, the crater is imaged as an ellipse and the scale changes in the landing camera. In this paper, a robust and efficient crater detection and recognition algorithm for fusing the information of sequence images for pose estimation is designed, which can be used in both flying in orbit around and landing phases. Our method consists of two stages: stage 1 for crater detection and stage 2 for crater recognition. In stage 1, a single-stage network with dense anchor points (dense point crater detection network, DPCDN) is conducive to dealing with multi-scale craters, especially small and dense crater scenes. The fast feature-extraction layer (FEL) of the network improves detection speed and reduces network parameters without losing accuracy. We comprehensively evaluate this method and present state-of-art detection performance on a Mars crater dataset. In stage 2, taking the encoded features and intersection over union (IOU) of craters as weights, we solve the weighted bipartite graph matching problem, which is matching craters in the image with the previously identified craters and the pre-established craters database. The former is called “frame-frame match”, or FFM, and the latter is called “frame-database match”, or FDM. Combining the FFM with FDM, the recognition speed is enabled to achieve real-time on the CPU (25 FPS) and the average recognition precision is 98.5%. Finally, the recognition result is used to estimate the pose using the perspective-n-point (PnP) algorithm and results show that the root mean square error (RMSE) of trajectories is less than 10 m and the angle error is less than 1.5 degrees.
Highlights
Accepted: 30 August 2021Soft landing on the surface of a planet is one of the key technologies for space exploration missions and requires high positioning accuracy
Many researchers have studied crater detection methods and established a crater database of the Moon, Mars, and other planets through artificial or automatic detection methods [8,24,25,26,27]; the Bandeira Mars Crater Database is used for experimental data (Figure A1)
Detection and recognition methods used in existing crater-based pose estimation systems were analyzed in this paper, and a crater detection and recognition method consisting of two stages was designed
Summary
Accepted: 30 August 2021Soft landing on the surface of a planet is one of the key technologies for space exploration missions and requires high positioning accuracy. The pose-estimation method based on inertial navigation has accumulated errors, and the landing error is within a few kilometers [1], while high-precision landing missions require landing errors of less than 100 m or even 10 m. The optical flow method or feature-tracking method is a kind of TRN but cannot give the absolute pose [3]. Another method of TRN matches the image obtained by the image sensor with the image of the vicinity of the flight area and the stored terrain feature database to calculate the absolute pose. The NEAR mission successfully proved that high-precision pose information can be obtained using crater features [4]. Crater-based TRN is possibly one of the most suitable solutions for pose estimation in soft landing missions
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