arXiv:2607. 10822v1 Announce Type: new Abstract: Indoor spatial understanding remains a fundamental challenge for intelligent systems operating in physical environments.
arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
arXiv:2409. 00078v2 Announce Type: replace-cross Abstract: As a large number of Internet of Things (IoT) devices are deployed in the field, there arises huge potential of edge computing for indoor localization on those devices.
The paper introduces a sparse topometric graph for indoor navigation that encodes local 3D geometry in nodes and distance‑weighted edges for traversable connectivity. A learned graph‑matching network aligns subgraph structures across different sessions, enabling robust point‑goal navigation even with scene changes, sensor variations, or limited overlap. Experiments on Gibson and 3D‑FRONT datasets show higher success rates and a memory reduction of over two orders of magnitude compared to point‑level maps.
The paper explores person identification using millimeter‑wave point clouds beyond traditional gait analysis, focusing on seven activities of daily living (ADLs). It introduces the mm‑ADL dataset of 11 subjects and proposes an activity‑conditioned framework that routes each clip to an activity‑specific identity expert via a supervised mixture of experts. Experiments show that hard routing improves closed‑set ID accuracy from 62.1% to 68.0% and significantly boosts re‑identification metrics, demonstrating the benefit of activity context under controlled indoor conditions.
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
The paper presents a method for reconstructing building footprints using only wireless channel measurements. It introduces a two‑stage hierarchical framework: first, a physics‑grounded wireless foundation model converts each channel measurement into a scene representation; second, a diffusion model jointly integrates multiple representations to infer and sample the posterior distribution over building occupancy. Experiments show that this approach can accurately recover building footprints and that both the physics‑grounded representations and joint posterior inference are essential for performance.
arXiv:2609.17204v1 Announce Type: cross Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), colle...
The paper introduces CluProp, a framework that treats varied‑density clustering in high‑dimensional spaces as a label propagation process over neighborhood graphs. By combining density‑based ideas with graph connectivity, it offers a deterministic propagation strategy that reduces parameter sensitivity and scales efficiently to millions of points. CluProp is agnostic to distance metrics and consistently outperforms existing baselines in accuracy while processing large datasets in minutes.
arXiv:2410. 08238v2 Announce Type: replace-cross Abstract: We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks.
iOSPointMapper is a mobile app that performs real‑time, privacy‑conscious sidewalk mapping using on‑device semantic segmentation, LiDAR depth estimation, and fused GPS/IMU data on recent iPhones and iPads. It detects and localizes sidewalk‑relevant features such as traffic signs, traffic lights, and poles, and includes a user‑guided annotation interface for validating outputs before submission. The anonymized data is transmitted to the Transportation Data Exchange Initiative (TDEI), where it integrates with broader multimodal transportation datasets, and evaluations show the app’s potential for enhanced pedestrian mapping.
The paper presents a six‑month dataset of indoor air‑quality measurements from low‑cost and reference sensors, collected at five locations with contextual metadata. It defines four evaluation scenarios—reference‑efficient, location‑transfer, long‑term drift, and event‑conditioned—to test spatial generalization and robustness to distribution shifts. A lightweight temporal model that compresses input windows and fuses residual temporal and feature information achieves strong calibration performance across all scenarios while keeping edge‑inference cost low.