As global efforts to explore the Moon intensify, the need for high-quality 3D lunar maps becomes increasingly critical—particularly for long-distance missions such as NASA’s Endurance mission concept, in which a rover aims to traverse 2,000 km across the South Pole–Aitken basin. Kaguya TC (Terrain Camera) images, though globally available at 10 m/pixel, suffer from altitude inaccuracies caused by stereo matching errors and JPEG-based compression artifacts. This paper presents a method to improve the quality of 3D maps generated from Kaguya TC images, focusing on mitigating the effects of compression-induced noise in disparity maps. We analyze the compression behavior of Kaguya TC imagery, and identify systematic disparity noise patterns, especially in darker regions. In this paper, we propose an approach to enhance 3D map quality by reducing residual noise in disparity images derived from compressed images. Our experimental results show that the proposed approach effectively reduces elevation noise, enhancing the safety and reliability of terrain data for future lunar missions.

To achieve longer driving distances, planetary robotics missions require accurate localization to counteract position uncertainty. Freedom and precision in driving allows scientists to reach and study sites of interest. Typically, rover global localization has been performed manually by humans, which is accurate but time-consuming as data is relayed between planets. This paper describes a global localization algorithm that is run onboard the Perseverance Mars rover. Our approach matches rover images to orbital maps using a modified census transform to achieve sub-meter accurate, near-human localization performance on a real dataset of 264 Mars rover panoramas. The proposed solution has also been successfully executed on the Perseverance Mars Rover, demonstrating the practicality of our approach.

Earthquakes occur when stress in the Earth exceeds the strength of the surrounding host rock. Earthquakes can occur naturally or be induced by human activity [1]. In this paper, we address earthquakes occurring on fault systems that are driven by plate tectonics. Motion of the Earth's tectonic plates causes deformation of the Earth's crust. As this deformation occurs, strain accumulation can be released seismically in earthquakes, or aseismically as creep along faults and in bulk deformation of the crust. Understanding how strain is distributed along fault systems, how stress transfers through the crust along fault zones between earthquakes, and the fraction of aseismic versus seismic deformation, is key to assessing earthquake hazard for mitigating losses from future events [2].

Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.

We study the problem of localizing a balloon in the atmosphere of Saturn's moon Titan by registering onboard imagery with orbital imagery. This is critical for both autonomous navigation purposes and acquisition and sampling of scientifically interesting sites. Because of Titan's atmospheric opacity, we require the ability to match combinations of visible, infrared (IR) and synthetic aperture radar (SAR) images. For both localization and direct use as a multi-modal data product for science analysis, match results must be sub-pixel accurate. We demonstrate the feasibility of matching orbital SAR data to visible and IR imagery and outline a framework for using this data as a navigation product. We demonstrate a technique to compensate for local distortions to enable accurate data registration in spite of differences in sensor return and imaging geometry. Finally, we show match results using both terrestrial imagery and the limited amount of available Titan data acquired by the Cassini orbiter and Huygens probe.

In this paper, we present the vision-aided inertial navigation (VISINAV) algorithm that enables precision planetary landing. The vision front-end of the VISINAV system extracts 2-D-to-3-D correspondences between descent images and a surface map (mapped landmarks), as well as 2-D-to-2-D feature tracks through a sequence of descent images (opportunistic features). An extended Kalman filter (EKF) tightly integrates both types of visual feature observations with measurements from an inertial measurement unit. The filter computes accurate estimates of the lander's terrain-relative position, attitude, and velocity, in a resource-adaptive and hence real-time capable fashion. In addition to the technical analysis of the algorithm, the paper presents validation results from a sounding-rocket test flight, showing estimation errors of only 0.16 m/s for velocity and 6.4 m for position at touchdown. These results vastly improve current state of the art for terminal descent navigation without visual updates, and meet the requirements of future planetary exploration missions.

Many of NASA's planned missions for the coming decade will require a pinpoint landing (PPL) capability, whether for sample acquisition and return or for precise insertion into hazardous but scientifically interesting terrain. Thus, a robust spacecraft-based position estimation system is a critical near term need. We present in this paper a vision-based system, which provides a low power, low cost, high accuracy solution with flight proven hardware. The key components of our image based localization approach are: (1) real-time detection of landmarks in descent imagery, (2) real-time matching of detected landmarks to a stored database, and (3) robust estimation of spacecraft state (position, attitude and velocity) from matched 2D-3D data. We present an analysis of the performance and noise sensitivity of our system and show that the suggested technology is able to deliver a spacecraft to within 100 meters of a pre-selected landing site in a typical Mars landing scenario.

Dynamic scene perception is currently limited to detection of moving objects from a static platform or scenes with flat backgrounds. We discuss novel real-time methods to segment moving objects in the motion field formed by a moving camera/robotic platform with mostly translational motion. Our solution does not explicitly require any egomotion knowledge, thereby making the solution applicable to mobile outdoor robot problems where no IMU information is available. We address two problems in dynamic scene perception on the move, first using only 2D monocular grayscale images, and second where 3D range information from stereo is also available. Our solution involves real-time optical flow computations, followed by optical flow field preprocessing to highlight moving object boundaries. In the case where range data from stereo is computed, a 3D optical flow field is estimated by combining range information with 2D optical flow estimates, followed by a similar 3D flow field preprocessing step. A segmentation of the flow field using fast flood filling then identifies every moving object in the scene with a unique label. This novel algorithm is expected to be the critical first step in robust recognition of moving vehicles and people from mobile outdoor robots, and therefore offers a robust solution to the problem of dynamic scene perception in the presence of certain kinds of robot motion. It is envisioned that our algorithm will benefit robot scene perception in urban environments for scientific, commercial and defense applications. Results of our real-time algorithm on a mobile robot in a scene with a single moving vehicle are presented.

Estimation of camera pose from an image of n points or lines with known correspondence is a thoroughly studied problem in computer vision. Most solutions are iterative and depend on nonlinear optimization of some geometric constraint, either on the world coordinates or on the projections to the image plane. For real-time applications, we are interested in linear or closed-form solutions free of initialization. We present a general framework which allows for a novel set of linear solutions to the pose estimation problem for both n points and n lines. We then analyze the sensitivity of our solutions to image noise and show that the sensitivity analysis can be used as a conservative predictor of error for our algorithms. We present a number of simulations which compare our results to two other recent linear algorithms, as well as to iterative approaches. We conclude with tests on real imagery in an augmented reality setup.