Terrain relative navigation can improve the precision of a spacecraft's position estimate by detecting global features that act as supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system that uses a convolutional neural network (CNN) and image processing methods to track the location of a simulated spacecraft with an extended Kalman filter (EKF). The CNN, called LunaNet, visually detects craters in the simulated camera frame and those detections are matched to known lunar craters in the region of the current estimated spacecraft position. These matched craters are treated as features that are tracked using the EKF. LunaNet enables more reliable position tracking over a simulated trajectory due to its greater robustness to changes in image brightness and more repeatable crater detections from frame to frame throughout a trajectory. LunaNet combined with an EKF produces a decrease of 60% in the average final position estimation error and a decrease of 25% in average final velocity estimation error compared to an EKF using an image processing-based crater detection method when tested on trajectories using images of standard brightness.
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
The Artemis program requires robotic and crewed lunar rovers for resource prospecting and exploitation, construction and maintenance of facilities, and human exploration. These rovers must support navigation for 10s of kilometers (km) from base camps. A lunar science rover mission concept - Endurance-A, has been recommended by the new Decadal Survey as the highest priority medium-class mission of the Lunar Discovery and Exploration Program, and would be required to traverse approximately 2000 km in the South Pole-Aitkin (SPA) Basin, with individual drives of several kilometers between stops fo…
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）
There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. Further, some of these proposed missions require autonomous driving and absolute localization in darkness. For example, the Endurance A mission proposes to drive 1200km of its total traverse at night. The lack of natural light available during such missions limits what can be used as visual landmarks and the range at which landmarks can be observed. In order for planetary rovers to traverse long ranges, onboard absolute localization is critical to the abi…
It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatme…
Vision-Language Model for Accurate Crater Detection
University of Technology of Troyes（图卢兹技术大学）；Hochschule Darmstadt（达姆施塔特应用科学大学）；GMV for European Space Agency（欧洲航天局GMV）；European Space Agency（欧洲航天局）；Technische Hochschule Würzburg-Schweinfurt（维尔茨堡-施韦因富特技术大学）
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Onboard localization capabilities for planetary rovers to date have used relative navigation, by integrating combinations of wheel odometry, visual odometry, and inertial measurements during each drive to track position relative to the start of each drive. At the end of each drive, a ground-in-the-loop (GITL) interaction is used to get a position update from human operators in a more global reference frame, by matching images or local maps from onboard the rover to orbital reconnaissance images or maps of a large region around the rover's current position. Autonomous rover drives are limited i…
Université de Montréal（蒙特利尔大学）；University of Toronto Scarborough（多伦多大学士嘉堡分校）；Canadian Institute for Theoretical Astrophysics（加拿大理论天体物理研究所）；University of Toronto（多伦多大学）；Arizona State University（亚利桑那州立大学）
Crater ellipticity determination is a complex and time consuming task that so far has evaded successful automation. We train a state of the art computer vision algorithm to identify craters in Lunar digital elevation maps and retrieve their sizes and 2D shapes. The computational backbone of the model is MaskRCNN, an "instance segmentation" general framework that detects craters in an image while simultaneously producing a mask for each crater that traces its outer rim. Our post-processing pipeline then finds the closest fitting ellipse to these masks, allowing us to retrieve the crater ellipti…
DEM Refinement and Validation on the Lunar Surface Using Shape-from-Shading with Chandrayaan-2 OHRC Imagery
School of Computer Science and Engineering, Manipal University（曼海姆大学计算机科学与工程学院）；Space Applications Centre (SAC), Indian Space Research Organisation (ISRO)（印度空间研究组织空间应用中心）
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 surface there are multiple challenges associated with Hazard Relative Navigation to ensure safe landings. This paper will focus on a passive autonomous hazard detection and avoidance sub-system to generate an initial assessment of possible landing regions for the guidance system. The system uses a single camera and the MobileNetV2 neural network architecture to detect and discern…
LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images
School of Computer Science and Engineering（计算机科学与工程学院）；Space Applications Centre (SAC)（空间应用中心）；Indian Space Research Organisation (ISRO)（印度空间研究组织）；Department of IoT and Intelligent Systems（物联网与智能系统系）；Department of Computer and Communication Engineering（计算机与通信工程系）
Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Department of Informatics, University of Oslo（信息学院，奥斯陆大学）；LunaSol Space LLC（LunaSol空间有限公司）；Department of Information Science and Electrical Engineering , Kyushu University（信息科学与电气工程学院，九州大学）
Adapting a Foundation Model for Lunar Surface Height Estimation
University of Technology of Troyes（特鲁瓦技术大学）；Hochschule Darmstadt（达姆施塔特应用科学大学）；European Space Agency（欧洲空间局）；Technische Hochschule Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用科学大学）
