Conference Agenda

Urban localization for autonomous driving requires accurate 6-DoF vehicle pose despite GNSS multipath, occlusions, and rapidly changing visibility. We fuse LiDAR, IMU, and GNSS in an error-state Kalman filter against a high-resolution (HR) map, aiming (i) to reduce LiDAR load without degrading accuracy and (ii) to improve robustness in building-sparse areas such as open junctions. The reference trajectory and HR map stem from a dedicated urban measurement campaign; Monte-Carlo simulations use ray-cast LiDAR, synthesized IMU, and GNSS tied to this trajectory so that only sensor noise is varied. A geometry-aware farthest-point sampling scheme prioritizes points informative for building/ground planes and pole-like structures, while an extended functional model introduces poles as additional vertical constraints. A retained-point rate of 10 % preserves trajectory-wide millimetrelevel and sub-milliradian accuracy, meeting in theory automotive requirements. Filter runtime is reduced by about 82 % relative

to the full LiDAR data. Compared with plane-only variants, the planes+poles configuration yields statistically significant but globally modest improvements in longitudinal, lateral, and yaw accuracy. More importantly, a sliding-window analysis reveals that it markedly stabilizes pose in plane-sparse junctions. Overall, the results suggest that task-aware subsampling preserves trajectory-wide performance while pole constraints add local robustness in challenging urban scenes; validation with real sensor logs remains necessary to confirm these accuracy margins, but the proposed filtering scheme shows promising potential for practical deployment.

Sensor data are increasingly used for monitoring and observation of spatiotemporal phenomena for diverse applications such as in flood management, urban traffic, air quality control, forest fire management, etc. Real time modelling and representation of such evolving phenomena is fundamental for efficient and timeliness decision-making processes. In the context of multisensory systems, where two phenomena (e.g.: air pollution index and windy condition) can both be sensed by networked sensors, analysing the relationship that hold between them is a major issue for decision making. Knowing if the pollution extent is expanding or contracting around a given spot or if it is within a windy zone can help in adopting more appropriate strategies. Sensing system equipped with rule-based reasoning engine to infer on spatiotemporal changes or topological relationship that holds between sensed phenomena with broad boundaries over time will provide decision-maker with adequate and non-ambiguous information. In this paper spatial changes and topological relationship about fuzzy-crisp object modelling the geometry of vague shape phenomena are conceptualized using an Extended Fuzzy Spatiotemporal Change Pattern (FESTCP) and a 5x5 Intersection model (I5x5M) respectively. The rule-based reasoning engine proposed in this paper is based on this conceptualisation. To evaluate our method, a simulated case study of air pollution in Quebec City is carried out. The results reveal that the proposed method captures well the spatiotemporal evolution of a given air pollution episode that served for an on-the-fly decision-making process in real life situations.

Reliable lane-level localization remains difficult for autonomous vehicles (AVs) when Global Navigation Satellite System (GNSS) observations are degraded by blockage, multipath, and non-line-of-sight reception in urban environments. This paper presents PointLoc, an Inertial Navigation System (INS)-centric locator aided by GNSS, monocular visual-inertial odometry (VIO), and High-Definition (HD) Vector Maps. The proposed method is formulated as an INS-centric error-state extended Kalman filter (EKF), in which the INS provides persistent state propagation, while GNSS, VIO, and map matching are incorporated as aiding updates according to their availability and reliability. This design preserves a unified position, velocity, and attitude solution and enables graceful degradation when some aiding sources become unavailable. The method is validated through real-vehicle experiments in Taichung Shuinan and Tainan Shalun under mixed GNSS conditions. The results show that PointLoc achieves the best overall full-route performance in Taichung Shuinan and remains broadly comparable to GNSS/INS/VIO, while still outperforming GNSS/INS, in Tainan Shalun. In the mapped GNSS-denied segment of Taichung Shuinan, PointLoc effectively suppresses vertical drift and substantially improves three-dimensional positioning. The mapped-road analysis further shows that the INS-centric design avoids the planar instability observed in a vision-centric benchmark and provides a more continuous localization solution.

Conventional laser scanning techniques (such as in a Terrestrial Laser Scanner or Mobile mapping), whether used in a static or mobile mode require the object of interest to remain stationary during the scanning stage. Any motion of the object during scanning results in the apparent distortions in the resulting point cloud. The authors in Goel and Lohani (2014b) proposed a motion correction technique to estimate the 3D geometry of a moving object, utilizing a fusion of inertial and GNSS (Global Navigation Satellite Systems) sensors and transformation of the resulting point cloud to an object body coordinate system (OBCS). This paper aims to carry out the experimental validation and performance analysis of the motion correction method. Field experiments are designed and conducted in three phases to verify the correctness of the method. Through this, the paper aims to uncover insights into the performance of the motion correction algorithm and provide the first experimental validation of the proposed technique.

Wearable sensors are essential for gait analysis outside of traditional laboratory environments. However, selection of the right sensor technology involves several trade-offs. Inertial Measurement Units (IMUs) offer high temporal resolution which are ideal for detecting gait events but they suffer from drift. Ultra-Wideband (UWB) provides stable spatial data, but are less precise for detecting event timing. This paper presents a comparative study of three distinct foot-mounted sensor methodologies for heel strike detection and cadence estimation: (1) IMU-Only approach, (2) UWB-Only approach, and (3) a multi-sensor IMU+UWB fusion approach. Each method is evaluated against a camera-based ground truth system using data from four subjects. Results show the IMU-Only method is inconsistent, with moderate event precision (Avg. F1: 0.798), temporal accuracy (Avg. MAE: 47.99 ms), and subject-dependent cadence accuracy (Avg. Acc: 89.59%). The UWB-Only method provides robust event detection (Avg. F1: 0.811) with similar temporal error (Avg. MAE: 49.0 ms) but is exceptionally accurate for cadence estimation (Avg. Acc: 96.94%). The IMU+UWB fusion approach achieves the highest event precision (Avg. Precision: 0.835) and the best temporal accuracy (Avg. MAE: 46.51 ms), while also maintaining robust cadence accuracy (Avg. Acc: 95.62%). In conclusion, while the UWB-Only method is ideal for cadence-only applications, the IMU+UWB fusion approach provides the best overall balance of high event precision, superior temporal accuracy, and reliable cadence estimation.

Accurately registering building footprints from heterogeneous datasets with LiDAR data remains a critical challenge in urban mapping and 3D reconstruction. The objective of this work is to register source data, defined as 2D cadastral vector footprints from structured, regularized, or manually-verified datasets to target building footprints derived from classified aerial LiDAR. LiDAR provides direct 3D information with precise footprint positioning and high spatial resolution, enabling a geometrically reliable representation of dense 3D structures. Conversely, source datasets are not always up-to-date, and may exhibit geometric distortions such as translational offsets, rotational deviations, or local deformations, yet they remain valuable due to their structured organization and metadata content. To enhance geometric fidelity while preserving semantic structure, we propose a practical framework for non-rigid polygon registration that adjusts the geometry of cadastral footprints toward LiDAR-derived targets. The framework consists of two core components: (1) establishing correspondences between source and target polygons, and (2) minimizing a robust distance function that governs the registration process. Three deformation models are introduced: a rigid model allowing translations only, a semi-rigid model allowing deformations while keeping the overall structure of source footprints, and a non-rigid model allowing rotations. We evaluate our method by aligning real cadastral datasets to aerial LiDAR data. The results confirm the effectiveness and robustness of the proposed framework in the context of 2D polygonal cadastral data. This work thus represents the first practical solution for non-rigid polygon registration in this domain.

This paper presents a mask-aware multi-stage depth enhancement framework for digital documentation of historical timber surfaces using RGB–Stereo-IR fusion. Accurate geometric recording of aged wood features such as wooden knots remains challenging due to uneven illumination and weak texture. The proposed pipeline, which aims to stabilise depth geometry under uneven illumination and low-texture surface conditions, integrates object detection, instance segmentation and confidence-guided depth refinement across three stages: (A) TV(total variation)-regularized mask-aware refinement, (B) confidence-weighted multi-view fusion, and (C) patch-based stereo reconstruction. Experiments on historical timber beams under varying illumination demonstrate improved depth completeness and geometric consistency, achieving a residual standard deviation below 0.6 mm in bright scenes and stable reconstruction in low-light conditions. The framework offers a practical solution for depth reconstruction of cultural heritage timber, supporting more reliable feature detection and analysis.