arXiv subjects

Emilia Szymańska

Publications and source records attributed to Emilia Szymańska.

3 recordsLinked to original sources

arXiv2026

Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization

Automated progress monitoring on construction sites is an active area of research and development. Robot and human-carried mapping systems have been developed to build 3D maps of building and infrastructure projects. While LiDAR-based mapping systems achieve high accuracy, the cost of LiDAR can be prohibitive. Consumer-grade cameras with wide field of view ("360 cameras") combined with embedded inertial measurement units (IMUs) provide a cost-effective alternative. To support change detection and progress monitoring, highly accurate visual simultaneous localization and mapping (SLAM) and floor plan localization systems are required. In this paper, we present a high-quality dataset collected at an active construction site, which captures realistic challenges such as variable lighting conditions, moving workers, fast motions, and repetitive structures. The dataset offers thirty visual-inertial sequences recorded across ten floors over an eight-month period of the construction project. Ground truth trajectories are collected using a high quality LiDAR-inertial SLAM system rigidly attached to the 360 camera. Additionally, we report the results of an open research challenge evaluating the best visual SLAM and localization systems from around the world. The Challenge attracted substantially higher participation in SLAM, with 62 teams compared to 22 in floor plan localization, which suggests that this task receives less attention from researchers and motivates further exploration through proposed dataset and challenge, available at: https://hilti-trimble-challenge.com/dataset-2026.

cs.RO↗

arXiv2026

Reading the Room: Scale-ambiguous Semantic Registration of Sparse VSLAM Maps to Floor Plans in Partially Built Environments

Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan. Visual capture is attractive for its low cost and availability, but aligning a visual simultaneous localization and mapping (VSLAM) map to a floor plan is difficult; VSLAM recovers geometry only up to an unknown similarity transform, the sparse point cloud is cross-modal to the symbolic floor plan, sites are only partially built during capture, and construction sites are visually repetitive, degrading map quality while removing distinctive features. Existing methods address at most two of these challenges. We thus introduce Reading the Room(RR), a global-local approach to register scale-ambiguous, sparse, semantic VSLAM maps to partially built floor plans. Our key idea is to define a single robust, differentiable, class-balanced semantic-geometric energy function that governs all stages of the pipeline. We develop two global initialization schemes, leveraging a single inaccurate user-provided correspondence if provided, or estimating the initialization globally using a cross-modal matching network. Both schemes are designed to find initial candidates with respect to the energy function, which is then directly optimized for local refinement. We evaluate RR on 33 sequences across three active construction sites against classical and learning-based baselines, achieving a 4.5x improvement in mean absolute trajectory error over the strongest baseline in the general unconstrained setting, which increases to 7.6x when an anchor is provided.

cs.RO↗

arXiv2026

RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction

Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an image stream. However, such predictions are prone to becoming inconsistent over long trajectories, specifically in demanding environments with repetitive structures, weak textures and dynamic objects or people. One way to mitigate those challenges is to use an omnidirectional camera, which provides wide spatial coverage and captures richer visual information. Yet, the majority of models do not offer support for 360-degree imagery or require additional fine-tuning. To bridge these two aspects, we present RIGOR: a large-scale reconstruction pipeline for gravity-aligned omnidirectional videos that retains a frozen feed-forward perspective backbone and exploits each panorama as a four-view virtual rig. The rig structure is used to detect and repair locally inconsistent predictions, to retrieve loop closures through cyclic four-view consensus, and to geometrically verify candidate revisits before global optimization. Verified constraints drive a Sim(3) pose graph that corrects accumulated rotation, translation, and scale drift along the sequence. We demonstrate that the proposed consistency mechanisms improve both trajectory accuracy and reconstructed geometry over a feed-forward baseline on challenging construction-site sequences. The code is made available under this link: https://github.com/TangentH/RIGOR.

cs.CV↗