Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigation

2024-08-24 · Jianing Song, Nabil Aouf, Duarte Rondao, Christophe Honvault, Luis Mansilla

The Lunar landing has drawn great interest in lunar exploration in recent years, and autonomous lunar landing navigation is fundamental to this task. AI is expected to play a critical role in autonomous and intelligent space missions, yet human experts question the reliability of AI solutions. Thus, the \gls{xai} for vision-based lunar landing is studied in this paper, aiming at providing transparent and understandable predictions for intelligent lunar landing. Attention-based Darknet53 is proposed as the feature extraction structure. For crater detection and navigation tasks, attention-based YOLOv3 and attention-Darknet53-LSTM are presented respectively. The experimental results show that the offered networks provide competitive performance on relative crater detection and pose estimation during the lunar landing. The explainability of the provided networks is achieved by introducing an attention mechanism into the network during model building. Moreover, the PCC is utilised to quantitively evaluate the explainability of the proposed networks, with the findings showing the functions of various convolutional layers in the network.

📄 PDF Abstract BibTeX arXiv:2408.13587

Code (0)

등록된 구현이 없습니다.

Tasks

Pose Estimation

Methods 이 논문이 사용한 방법론

Attention 설명 없음

BNB Customer Service Number +1-833-534-1729 설명 없음

Average Pooling 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

Logistic Regression Logistic Regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression,…

Batch Normalization 설명 없음

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Vision-Language Model for Accurate Crater Detection

2026-01-12 · Patrick Bauer, Marius Schwinning, Florian Renk, Andreas Weinmann 외 arxiv

The European Space Agency (ESA), driven by its ambitions on planned lunar missions with the Argonaut lander, has a profound interest in reliable crater detection, since craters pose a risk to safe lunar landings. This ta…

parameter-efficient fine-tuning

Detection and Initial Assessment of Lunar Landing Sites Using Neural Networks

2022-07-23 · Daniel Posada, Jarred Jordan, Angelica Radulovic, Lillian Hong 외

Robotic and human lunar landings are a focus of future NASA missions. Precision landing capabilities are vital to guarantee the success of the mission, and the safety of the lander and crew. During the approach to the su…

Deep Learning-Based Lunar Crater Terrain Relative Navigation

2026-06-10 · Batu Candan, Simone Servadio arxiv

Accurate position estimation is crucial for the successful implementation of future lunar landings using autonomous vehicles, especially in dangerous environments with sparse terrain features. In this paper, we propose a…

Autonomous Vehicles Pose Estimation

Camera-Pose Robust Crater Detection from Chang'e 5

2024-06-07 · Matthew Rodda, Sofia McLeod, Ky Cuong Pham, Tat-Jun Chin

As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact crater…

AI-Enabled Crater-Based Navigation for Lunar Mapping

2025-09-25 · Sofia McLeod, Chee-Kheng Chng, Matthew Rodda, Tat-Jun Chin arxiv

Crater-Based Navigation (CBN) uses the ubiquitous impact craters of the Moon observed on images as natural landmarks to determine the six degrees of freedom pose of a spacecraft. To date, CBN has primarily been studied i…