With the recent discovery of water-ice and lava tubes on the Moon and Mars along with the development of in-situ resource utilization (ISRU) technology, the recent planetary exploration has focused on rover (or lander)-based surface missions toward the base construction for long-term human exploration and habitation. However, a 3D terrain map, mostly based on orbiters’ terrain images, has insufficient resolutions for construction purposes. In this regard, this paper introduces the visual simultaneous localization and mapping (SLAM)-based robotic mapping method employing a stereo camera system on a rover. In the method, S-PTAM is utilized as a base framework, with which the disparity map from the self-supervised deep learning is combined to enhance the mapping capabilities under homogeneous and unstructured environments of planetary terrains. The overall performance of the proposed method was evaluated in the emulated planetary terrain and validated with potential results.

In planetary construction, the semiautonomous teleoperation of robots is expected to perform complex tasks for site preparation and infrastructure emplacement. A highly detailed 3D map is essential for construction planning and management. However, the planetary surface imposes mapping restrictions due to rugged and homogeneous terrains. Additionally, changes in illumination conditions cause the mapping result (or 3D point-cloud map) to have inconsistent color properties that hamper the understanding of the topographic properties of a worksite. Therefore, this paper proposes a robotic construction mapping approach robust to illumination-variant environments. The proposed approach leverages a deep learning-based low-light image enhancement (LLIE) method to improve the mapping capabilities of the visual simultaneous localization and mapping (SLAM)-based robotic mapping method. In the experiment, the robotic mapping system in the emulated planetary worksite collected terrain images during the daytime from noon to late afternoon. Two sets of point-cloud maps, which were created from original and enhanced terrain images, were examined for comparison purposes. The experiment results showed that the LLIE method in the robotic mapping method significantly enhanced the brightness, preserving the inherent colors of the original terrain images. The visibility and the overall accuracy of the point-cloud map were consequently increased.

The recent discovery of water ice in the lunar polar shadowed regions (PSRs) has driven interest in robotic exploration, due to its potential utilization to generate water, oxygen, and hydrogen that would enable sustainable human exploration in the future. However, the absence of direct sunlight in the PSRs poses a significant challenge for the robotic operation to obtain clear images, consequently impacting crucial tasks such as obstacle avoidance, pathfinding, and scientific investigation. In this regard, this study proposes a visual simultaneous localization and mapping (SLAM)-based robotic mapping approach that combines dense mapping and low-light image enhancement (LLIE) methods. The proposed approach was experimentally examined and validated in an environment that simulated the lighting conditions of the PSRs. The mapping results show that the LLIE method leverages scattered low light to enhance the quality and clarity of terrain images, resulting in an overall improvement of the rover’s perception and mapping capabilities in low-light environments.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model.

Climate changes and coastal development pose growing risks to coastal roadways constructed on flat and low-elevation terrains near retreating shorelines. Although GIS has been widely used for shoreline change analysis and roadway management, significant limitations remain in accurately analyzing shoreline changes relative to roadways and integrating the analysis results into roadway spatial databases in the Geographic Information System (GIS). In this regard, this study proposes a novel geospatial approach that integrates the linear referencing system (LRS) with the vector-offset based analysis method for shoreline change. The LRS, implemented in GIS, defines the specific positions of a roadway using relative distances from predefined referents. Vector offsets, representing the shortest distance and direction from historical shorelines to the roadway, are then employed to analyze shoreline changes. The proposed approach was applied to a coastal roadway experiencing significant shoreline changes driven by climate change and the construction of coastal infrastructure. The results demonstrate the effectiveness of the proposed approach in analyzing shoreline retreat caused by coastal infrastructure development, as well as shoreline accretion following the installation of erosion control structures. These results, which closely reflect the actual erosion pattern, indicate that the proposed approach can effectively support planning for roadway maintenance and reinforcement.