Abstract. Professional mobile mapping systems achieve centimeter-level accuracy through the tight integration of navigation-grade IMUs, survey-grade GNSS, and calibrated laser scanners. This paper assesses the relative and absolute accuracy of a vehicle-mounted test platform that combines a NovAtel SPAN tightly coupled GNSS/IMU with a mid-grade Velodyne VLP-16 LiDAR, evaluated along a 1.2 km test loop on The Ohio State University campus. The mobile-cloud georeferencing is driven entirely by the SPAN-processed GNSS/IMU trajectory, post-processed in NovAtel Inertial Explorer; three additional survey-grade PPK GNSS receivers are processed independently and serve as cross-checks on the trajectory. Direct georeferencing is performed in the standard ECEF formulation with per-point trajectory interpolation. An independent reference point cloud of the same area is acquired with a Leica RTC360 terrestrial laser scanner, registered and tied down to GNSS-derived ground control so that the TLS cloud carries an independent absolute geodetic datum. Absolute accuracy is then evaluated directly: identifiable features on building façades are coordinated independently in each cloud, and the coordinate differences between the mobile-cloud and TLS positions of the same features quantify the absolute georeferencing error of the directly georeferenced mobile cloud. Relative accuracy is evaluated separately from the internal geometry of each cloud: structure dimensions, inter-feature distances, and inter-feature angles are measured in the mobile cloud and in the TLS and compared, isolating the shape-preservation performance of the platform from any constant offset between the two reference frames. An auxiliary SHARE SLAM S20 handheld scanner mounted on the same vehicle is described as supplementary platform context; its data is not used in the accuracy assessment due to unreliable GNSS/IMU/SLAM integration at vehicle speeds.
R. Tamimi, Baris Süleymanoğlu, A. Elashry et al.· The International Archives o...· 0 citations
Abstract. This paper presents a deep learning and classical computer vision framework for cross-view geolocalization using 360-degree multi-perspective view (PV) images and an offline global map. Recent studies on cross-view geolocalization typically rely on deep learning models to localize panoramic PV images by matching them with reference satellite imagery. However, such approaches face practical limitations in real-world deployments, due to their dependence on large-scale GPU resources and the need to store extensive satellite image datasets. To address these challenges, we propose BEV-LOC, a lightweight and real-time cross-view geolocalization method. BEV-LOC employs Bird’s Eye View (BEV) encoder that learns to transform 360-degree multi-PV images into a local high-definition (HD) BEV map. The localization is then performed using Intersection Over Union (IoU)-based template matching with an offline global map. Our architecture achieves real-time performance at 30 FPS without the need for high-end GPU hardware and delivers a high positioning accuracy of 1.2 meters.
J. Kwag, C. Toth, Alper Yilmaz· The International Archives o...· 0 citations