Conference Agenda

In Indoor Mobile Mapping Systems (iMMS) the trajectory estimation is implemented by the SLAM (Simultaneous Localization and Mapping) algorithm. By assuming a fixed environment surrounding the instrument, the algorithm relies on stable geometries to establish the trajectory. Drift effects represent the main source for errors and affect the trajectory estimation. These effects can be magnified in feature-deficient or degenerate environments, where the variation of geometrical elements can be minimal, as in the case of tunnels. In this context, difficult environments such as tunnels are suitable for the implementation of alternative algorithms for the trajectory estimation. Considering this kind of scenario, the contribution has the twofold objective of evaluating the results of two trajectory estimation methods, in terms of trajectory drift, with reference to an indoor SLAM-based MMS, and to establish a repeatable methodology to do so. A novel algorithm for the trajectory estimation, not just relying on geometrical SLAM algorithm, but also taking advantage of reflectance images coming from LiDAR sensors mounted on the system, is considered.

The case study is a 200 m long branch of a motor-way tunnel, with a diameter of 15 m. The test is further subdivided by computing all trajectories with different constraining strategies, first without any constraints, then considering global optimisation, loop closure and static control scans, to replicate typical realistic scenarios in tunnel mapping. The results of this work highlight how the novel reflectance-aided SLAM algorithm is beneficial in terms of drift reduction in the estimated trajectories.

This presentation addresses the challenge of range errors in point clouds of road signs captured by Mobile Mapping Systems (MMS) equipped with phase-shift laser scanners.

Under certain conditions, retroreflective materials cause range errors in point clouds. Previous studies have proposed mitigation techniques for range errors caused by sensor saturation in TOF systems, but similar studies on phase-shift systems are scarce. In addition, existing road sign detection methods assume accurate point representation, making them ineffective when sign points are displaced.

To overcome this limitation, we developed a detection method that first extracts road signs through point cloud visualization and then identifies range errors based on the standard deviation of relative distances from reference emission points.

The proposed approach was validated using 5 km of driving data collected on general roads. Results show that 32 road signs were extracted, and 26 were correctly detected as exhibiting range errors, achieving 100% agreement with manual visual assessment.

This study demonstrates the effectiveness of the proposed detection method and its potential for improving the reliability of identifying range errors of road signs on general roads.

RTK-SLAM systems integrate simultaneous localization and mapping (SLAM) with real-time kinematic (RTK) GNSS positioning, promising both relative consistency and globally referenced coordinates for efficient georeferenced surveying. A critical and underappreciated issue is that the standard evaluation metric, Absolute Trajectory Error (ATE), first fits an optimal rigid-body transformation between the estimated trajectory and reference before computing errors. This so-called SE(3) alignment absorbs global drift and systematic errors, making trajectories appear more accurate than they are in practice. We present a geodetically referenced dataset and evaluation methodology that expose this gap. A key design principle is that the RTK receiver is used solely as a system input, while ground truth is established independently via a geodetic total station. This separation is absent from all existing datasets, where GNSS typically serves as (part of) the ground truth. The dataset is collected with a handheld RTK-SLAM device, comprising two scenes. We evaluate LiDAR-inertial, visual-inertial, and LiDAR-visual-inertial RTK-SLAM systems alongside standalone RTK, reporting direct global accuracy and SE(3)-aligned relative accuracy to make the gap explicit. Results show that SE(3) alignment can underestimate absolute positioning error by up to 76\%. RTK-SLAM achieves centimeter-level absolute accuracy in open-sky conditions and maintains decimeter-level global accuracy indoors, where standalone RTK degrades to tens of meters. The dataset, calibration files, and evaluation scripts are made publicly available. The dataset, calibration files, and evaluation scripts are publicly available at https://rtk-slam-dataset.github.io/

Scene reconstruction and novel view synthesis from calibrated multi-view images still attracts a lot of attention in computer vision and graphics. However, the assumption that images are noise-free rarely holds in real-world scenarios where adverse weather conditions are inevitable. Being a part of our environment, we are particularly interested in rain as dynamic semi-transparent occlusion which imposes challenges to a complete and accurate geometry of the underlying features. More precisely, we qualitatively and quantitatively analyze the photometric image quality under rainy conditions generated by radiance field methods, namely: Neural Radiance Fields (NeRFs), 3D Gaussian Splatting (3DGS) and 2D Gaussian Splatting (2DGS) due to the different geometric representation. To assess the impact of rain to the scene reconstruction we consider raindrops and streaks captured with illumination variation as well as occlusion masks with different coverage. The evaluation is based on comparing 2D image metrics of the rendered novel views without and with masks. The experiments and results show that 3DGS achieves highest rendering fidelity in all scenarios without and with masks with SSIM of 0.724 and LPIPS of 0.291, followed by 2DGS with slightly lower scores, while NeRF exhibits lowest correspondence with the input images with SSIM of 0.584 and LPIPS of 0.384. We demonstrate the effectiveness of using masks to handle rain as transient element and radiance field methods’ ability to reliably approximate the geometry behind rain occlusions.

In recent years, many coastlines worldwide have retreated under the influence of storm surges and other extreme events, exacerbated by intensifying wave conditions in certain regions and seasons. Consequently, wave-dissipating units (e.g., tetrapods) have been widely deployed for coastal protection. In this paper, we propose a novel three-dimensional geometric method for extracting robust feature points from 3D point clouds to track tetrapod displacements and assess seawall safety. The model represents a tetrapod as four cylinders sharing a common center. By fitting this geometric model to the point cloud, we obtain parameters that allow us to derive multiple feature points—such as the intersections of conical surfaces—which can also be verified through alternative measurement techniques. These feature points serve as stable references for position comparison and displacement estimation. As this research is at an early stage, we have not yet collected field data from full-scale tetrapods. Instead, we conducted indoor experiments using a 3D depth camera (Microsoft Azure) in place of LiDAR, utilizing several high-fidelity resin tetrapod scale models (approximately 10 cm in height) as test subjects. The results demonstrate the feasibility of our method: when compared against total-station measurements, our approach yields highly accurate displacement estimates (averaging approximately 3 mm). This provides a solid foundation for the future deployment of 3D laser scanning in seawall monitoring.

As the technology of hydroacoustic sensors advances, there is a growing trend in the use of generated sonar images and point clouds in the analysis of the seabed and objects of anthropogenic origin in water bodies. In the context of cognitive and practical dimensions, obtaining data on sunken ships is of particular importance. Based on the data obtained from hydroacoustic sensors, it is possible to extract their geometric features. As a result, it is possible to develop digital repositories of wrecks, based on sonar and bathymetric data, among others, which in the future may enable the construction of integrated knowledge bases on underwater heritage. The purpose of the work was to extract the geometric features of the wreck of the Zawiszaczek located in the Puck Bay of the Baltic Sea. As part of the work, bathymetric measurements were planned, side-scan sonar and multibeam echosounder data were collected. Based on the acquired data, the geometric features of the wreck were extracted. The differences in the wreck's dimensions, as determined by sonar images obtained from different routes, did not exceed 0.25 m.