This paper addresses robot localization in GPS-denied indoor environments using a semantic reasoning approach with a vision-language model, achieving high accuracy with a composite loss and curriculum…

This paper introduces HoloGeo, an evidence-driven reasoning framework to mitigate landmark bias in Vision-Language Models, and establishes metrics and a benchmark to evaluate its effectiveness.

CIPER proposes a unified transformer framework to simultaneously perform cross-view image retrieval and precise 3-DoF pose estimation, overcoming the limitations of cascaded, separate methods.

GLAM-SLAM is a real-time, decoupled Gaussian-splatting SLAM system for large-scale outdoor scenes with a robust feature-based frontend and structured sparse mapping representation.

The paper introduces a novel two-stage framework to achieve robust, category-agnostic object localization in-context (ICL) by optimizing attention and minimizing localization error using reinforcement…

The paper introduces ERGeoBench, a comprehensive diagnostic benchmark designed to evaluate the fine-grained capabilities of multimodal large language models (MLLMs) for embodied geo-localization acros…

The paper proposes an uncertainty-aware, decentralized fusion layer for multi-UAV systems that significantly improves 3D localization robustness by incorporating neighbor constraints and handling faul…

The paper proposes MoEIoU, a novel mixture-of-experts based regression loss that adaptively models bounding-box localization errors, achieving superior convergence and accuracy in object detection.

The paper proposes RA-LWLM, a retrieval-augmented in-context localization framework that enables training-free, cross-scene wireless localization by externalizing scene-specific data into a fingerprin…

This paper presents a lightweight network for omnidirectional human detection and relative 2D pose estimation from planar LiDAR sequences using Space-Time Blocks.

This paper proposes a framework for multi-site channel charting in wireless networks using topological signal processing, enabling coherent integration of locally learned representations into a shared…

The paper introduces MetricScenes, a new large-scale, in-the-wild dataset, and demonstrates that fine-tuning existing geometry models on this dataset significantly mitigates the scale-collapse problem…

This paper explores the use of swarm and evolutionary computation methods for near-field localization in antenna arrays.

This paper introduces Path Consistency Scoring (PCS), a framework that evaluates router geolocation as a path-level consistency problem and produces a path consistency score based on a Hidden Markov M…

FLORO is a multimodal geospatial foundation model that learns transferable remote sensing representations from a small, diverse corpus, achieving strong performance across various sensor types and res…

The paper proposes a decentralized, witnessing-zone architecture that enhances Proof-of-Location (PoL) to provide robust, auditable evidence of physical events, thereby improving sensor data trustwort…

This paper proposes Point Cloud Upsampling through Patch-based Frequency Superposition (PUtPFS), an optimization-based approach for uniform point cloud upsampling without data dependency or training.

A regression-based approach is presented for Arabic dialect geolocation using a hierarchical neural architecture and spherical geodesic loss, achieving a median localization error of 481.2 km.