Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: : part II (Results and Discussion)

Acknowledgement. The authors of this paper would like to thank Kempe Foundation for its generous funding support to develop KORE, the workshop at the Teknikens Hus, Luleå, for their invaluable and unconditional support in helping with the fabrication of the KORE components and the organizers of the MINAR campaign comprising the UK Centre of Astrobiology, ICL Boulby Mine and STFC Boulby Underground Laboratory, UK. MPZ has been partially funded by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia ‘María de Maeztu’- Centro de Astrobiología (INTA-CSIC)Peer reviewedPostprin

Automatic Image Classification for Planetary Exploration

Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main problems in planetary exploration: rock image classification and hyperspectral image classification. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. The proposed feature method is flexible and can outperform manually selected features. In order to take advantage of the unlabelled rock images, we also propose self-taught learning technique to learn the feature representation from unlabelled rock images and then apply the features for the classification of the subclass of rock images. Since combining spatial information with spectral information for classifying hyperspectral images (HSI) can dramatically improve the performance, we first propose an innovative framework to automatically generate spatial-spectral features for HSI. Two unsupervised learning methods, K-means and PCA, are utilized to learn the spatial feature bases in each decorrelated spectral band. Then spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. In the second work for HSI classification, we propose to stack the spectral patches to reduce the spectral dimensionality and generate 2-D spectral quilts. Such quilts retain all the spectral information and can result in less convolutional parameters in neural networks. Two light convolutional neural networks are then designed to classify the spectral quilts. As the third work for HSI classification, we propose a combinational fully convolutional network. The network can not only take advantage of the inherent computational efficiency of convolution at prediction time, but also perform as a collection of many paths and has an ensemble-like behavior which guarantees the robust performance

Evaluation of 3D CNN Semantic Mapping for Rover Navigation

Terrain assessment is a key aspect for autonomous exploration rovers, surrounding environment recognition is required for multiple purposes, such as optimal trajectory planning and autonomous target identification. In this work we present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. Firstly, images are labeled with DeepLabv3+, which is an encoder-decoder Convolutional Neural Networl (CNN). Then, the labels obtained by the semantic segmentation are combined to stereo depth-maps in a Voxel representation. We evaluate our approach on the ESA Katwijk Beach Planetary Rover Dataset.Comment: To be presented at the 7th IEEE International Workshop on Metrology for Aerospace (MetroAerospace

Machine learning for the subsurface characterization at core, well, and reservoir scales

The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface earth resources, such as oil, gas, coal, geothermal energy, mining, and sequestration. With more data and more computation power, the traditional methods for subsurface characterization and engineering that are adopted by these industries can be automized and improved. New phenomenon can be discovered, and new understandings may be acquired from the analysis of big data. The studies conducted in this dissertation explore the possibility of applying machine learning to improve the characterization of geological materials and geomaterials. Accurate characterization of subsurface hydrocarbon reservoirs is essential for economical oil and gas reservoir development. The characterization of reservoir formation requires the integration interpretation of data from different sources. Large-scale seismic measurements, intermediate-scale well logging measurements, and small-scale core sample measurements help engineers understand the characteristics of the hydrocarbon reservoirs. Seismic data acquisition is expensive and core samples are sparse and have limited volume. Consequently, well log acquisition provides essential information that improves seismic analysis and core analysis. However, the well logging data may be missing due to financial or operational challenges or may be contaminated due to complex downhole environment. At the near-wellbore scale, I solve the data constraint problem in the reservoir characterization by applying machine learning models to generate synthetic sonic traveltime and NMR logs that are crucial for geomechanical and pore-scale characterization, respectively. At the core scale, I solve the problems in fracture characterization by processing the multipoint sonic wave propagation measurements using machine learning to characterize the dispersion, orientation, and distribution of cracks embedded in material. At reservoir scale, I utilize reinforcement learning models to achieve automatic history matching by using a fast-marching-based reservoir simulator to estimate reservoir permeability that controls pressure transient response of the well. The application of machine learning provides new insights into traditional subsurface characterization techniques. First, by applying shallow and deep machine learning models, sonic logs and NMR T2 logs can be acquired from other easy-to-acquire well logs with high accuracy. Second, the development of the sonic wave propagation simulator enables the characterization of crack-bearing materials with the simple wavefront arrival times. Third, the combination of reinforcement learning algorithms and encapsulated reservoir simulation provides a possible solution for automatic history matching

S5^{5}Mars: Semi-Supervised Learning for Mars Semantic Segmentation

Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, there is a lack of sufficient detailed and high-confidence data annotations, which are exactly required by most deep learning methods to obtain a good model. To address this problem, we propose our solution from the perspective of joint data and method design. We first present a newdataset S5Mars for Semi-SuperviSed learning on Mars Semantic Segmentation, which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a semi-supervised learning (SSL) framework for Mars image semantic segmentation, to learn representations from limited labeled data. Different from the existing SSL methods which are mostly targeted at the Earth image data, our method takes into account Mars data characteristics. Specifically, we first investigate the impact of current widely used natural image augmentations on Mars images. Based on the analysis, we then proposed two novel and effective augmentations for SSL of Mars segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost the model performance. Meanwhile, to fully leverage the unlabeled data, we introduce a soft-to-hard consistency learning strategy, learning from different targets based on prediction confidence. Experimental results show that our method can outperform state-of-the-art SSL approaches remarkably. Our proposed dataset is available at https://jhang2020.github.io/S5Mars.github.io/

POLAR3D: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation

We report on an effort that led to POLAR3D, a set of digital assets that enhance the POLAR dataset of stereo images generated by NASA to mimic lunar lighting conditions. Our contributions are twofold. First, we have annotated each photo in the POLAR dataset, providing approximately 23 000 labels for rocks and their shadows. Second, we digitized several lunar terrain scenarios available in the POLAR dataset. Specifically, by utilizing both the lunar photos and the POLAR's LiDAR point clouds, we constructed detailed obj files for all identifiable assets. POLAR3D is the set of digital assets comprising of rock/shadow labels and obj files associated with the digital twins of lunar terrain scenarios. This new dataset can be used for training perception algorithms for lunar exploration and synthesizing photorealistic images beyond the original POLAR collection. Likewise, the obj assets can be integrated into simulation environments to facilitate realistic rover operations in a digital twin of a POLAR scenario. POLAR3D is publicly available to aid perception algorithm development, camera simulation efforts, and lunar simulation exercises.POLAR3D is publicly available at https://github.com/uwsbel/POLAR-digital.Comment: 7 pages, 4 figures; this work has been submitted to the 2024 IEEE Conference on Robotics and Automation (ICRA) under revie